U.S. patent application number 16/820164 was filed with the patent office on 2020-09-17 for object detection using skewed polygons suitable for parking space detection.
The applicant listed for this patent is NVIDIA CORPORATION. Invention is credited to Junghyun Kwon, Dongwoo Lee, David Nister, Sangmin Oh, Berta Rodriguez Hervas, Hae-Jong Seo, Wenchao Zheng.
Application Number | 20200294310 16/820164 |
Document ID | / |
Family ID | 1000004730387 |
Filed Date | 2020-09-17 |
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United States Patent
Application |
20200294310 |
Kind Code |
A1 |
Lee; Dongwoo ; et
al. |
September 17, 2020 |
Object Detection Using Skewed Polygons Suitable For Parking Space
Detection
Abstract
A neural network may be used to determine corner points of a
skewed polygon (e.g., as displacement values to anchor box corner
points) that accurately delineate a region in an image that defines
a parking space. Further, the neural network may output confidence
values predicting likelihoods that corner points of an anchor box
correspond to an entrance to the parking spot. The confidence
values may be used to select a subset of the corner points of the
anchor box and/or skewed polygon in order to define the entrance to
the parking spot. A minimum aggregate distance between corner
points of a skewed polygon predicted using the CNN(s) and ground
truth corner points of a parking spot may be used simplify a
determination as to whether an anchor box should be used as a
positive sample for training.
Inventors: |
Lee; Dongwoo; (Seoul,
KR) ; Kwon; Junghyun; (San Jose, CA) ; Oh;
Sangmin; (San Jose, CA) ; Zheng; Wenchao; (San
Jose, CA) ; Seo; Hae-Jong; (San Jose, CA) ;
Nister; David; (Bellevue, WA) ; Rodriguez Hervas;
Berta; (San Francisco, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
NVIDIA CORPORATION |
Santa Clara |
CA |
US |
|
|
Family ID: |
1000004730387 |
Appl. No.: |
16/820164 |
Filed: |
March 16, 2020 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
62819544 |
Mar 16, 2019 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 2207/20084
20130101; G06T 2207/30264 20130101; G06T 7/40 20130101; G06T 17/30
20130101; G06T 2207/10021 20130101 |
International
Class: |
G06T 17/30 20060101
G06T017/30; G06T 7/40 20060101 G06T007/40 |
Claims
1. A computer-implemented method comprising: applying, to a neural
network, image data representative of a parking space; receiving,
using the neural network, data generated from the image data and
representative of displacement values to corner points of an anchor
box; determining corner points of a skewed polygon from the
displacement values to the corner points of the anchor box;
computing a first distance between the corner points of the skewed
polygon and ground-truth corner points of the parking space;
determining a sample rating based at least in part on the first
distance; and based on the sample rating being below a threshold
value, updating parameters of the neural network using the anchor
shape as a positive training sample.
2. The method of claim 1, wherein the first distance comprises a
minimum aggregate distance and wherein the sample rating is a
normalized version of the minimum aggregate distance.
3. The method of claim 1, wherein determining the sample rating
includes normalizing the first distance based at least in part on
an area of a polygon defined by the ground-truth corner points of
the parking space.
4. The method of claim 1, wherein the skewed polygon is a first
skewed quadrilateral and the anchor box is a second skewed
quadrilateral.
5. The method of claim 1, wherein the anchor shape is a data-driven
anchor box generated from one or more ground-truth samples.
6. The method of claim 1, wherein the first distance is a minimum
mean distance between different combinations of the corner points
of the skewed polygon with the ground-truth corner points of the
parking space.
7. The method of claim 1, wherein the corner points of the skewed
polygon comprise a first corner (A1), a second corner (A2), a third
corner (A3), and a fourth corner (A4); wherein the corner points of
the ground-truth corner points of the parking spot comprise a fifth
corner (B1), a sixth corner (B2), a seventh corner (B3), and an
eighth corner (B4); and wherein computing the first distance
comprises computing a first normalized aggregate distance from
distances (A1, B1), (A2, B2), (A3, B3), and (A4, B4); a second
normalized aggregate distance from distances (A1, B2), (A2, B3),
(A3, B4), and (A4, B1); a third normalized aggregate distance from
distances (A1, B3), (A2, B4), (A3, B1), and (A4, B2); and a fourth
normalized aggregate distance from distances (A1, B4), (A2, B1),
(A3, B2), and (A4, B3), and the first distance is a smallest of the
first normalized aggregate distance, the second normalized
aggregate distance, the third normalized aggregate distance, and
the fourth normalized aggregate distance.
8. A computer-implemented method comprising: applying, to a neural
network, sensor data representative of a field of view of at least
one sensor in an environment; receiving, from the neural network,
first data and second data generated from the sensor data, the
first data representative of displacement values to corner points
of an anchor shape and the second data representative of a
confidence value predicting a likelihood that the anchor shape
corresponds to a parking space in the field of view of the at least
one sensor; and based at least in part on the confidence value
exceeding a threshold value, determining corner points of a skewed
polygon that corresponds to the displacement values to the corner
points of the anchor shape.
9. The method of claim 8, wherein the anchor shape is of a
plurality of anchor shapes associated with a spatial element of the
neural network, and the neural network outputs for each given
anchor shape of the plurality of anchor shapes data representative
of displacement values to corner points of the given anchor shape
and a confidence value predicting a corresponding likelihood that
the given anchor shape corresponds to a corresponding parking space
in the field of view of the at least one sensor.
10. The method of claim 8, wherein the anchor shape is of a
plurality of anchor shapes associated with a grid of spatial
elements of the neural network, and the neural network outputs, for
each given anchor shape of the plurality of anchor shapes, data
representative of displacement values to corner points of the given
anchor shape and a confidence value predicting a corresponding
likelihood that the given anchor shape corresponds to a
corresponding parking space in the field of view of the at least
one sensor.
11. The method of claim 8, wherein the sensor data comprises image
data representative of a field of view of a camera.
12. The method of claim 8, wherein the anchor shape is of a
plurality of anchor shapes associated with one or more spatial
elements of the neural network, and the plurality of anchor shapes
comprise different shapes of skewed polygons.
13. The method of claim 8, wherein the skewed polygon is a first
skewed quadrilateral and the anchor shape is a second skewed
quadrilateral.
14. The method of claim 8 further comprising: receiving, from the
neural network, third data representative of confidence values
predicting likelihoods that the corner points of the skewed polygon
define an entrance to the parking space in the field of view of the
at least one sensor; selecting a subset of the corner points of the
anchor shape based at least in part on the confidence values; and
identifying the entrance to the parking space from the subset of
the corner points.
15. The method of claim 8, further comprising controlling one or
more operations of an autonomous vehicle based at least in part on
the corner points of the skewed polygon.
16. A computer-implemented method comprising: applying, to a neural
network, sensor data representative of a field of view of at least
one sensor in an environment; receiving, from the neural network,
first data and second data generated from the image data, the first
data representative of displacement values to corner points of an
anchor shape and the second data representative of confidence
values predicting likelihoods that the corner points of the anchor
shape define an entrance to a parking spot in the field of view of
the at least one sensor; selecting a subset of the corner points of
the anchor shape based on the confidence values; and identifying
the entrance to the parking spot from the subset of the corner
points.
17. The computer-implemented method of claim 16 further comprising:
determining corner points of a skewed polygon from the displacement
values to the corner points of the anchor shape; and controlling
one or more operations of an autonomous vehicle based at least in
part on the corner points of the skewed polygon and the entrance to
the parking spot.
18. The method of claim 16, wherein the anchor shape is a skewed
polygon.
19. The method of claim 16, wherein the anchor shape is of a
plurality of anchor shapes associated with a spatial element of the
neural network, and the neural network outputs for each given
anchor shape of the plurality of anchor shapes data representative
of displacement values to corner points of the given anchor shape
and confidence values predicting corresponding likelihoods that the
corner points of the given anchor shape define a given entrance to
a corresponding parking spot in the field of view of the at least
one sensor.
20. The method of claim 16, wherein the anchor shape is of a
plurality of anchor shapes associated with a grid of spatial
elements of the neural network, and the neural network outputs for
each given anchor shape of the plurality of anchor shapes data
representative of displacement values to corner points of the given
anchor shape and confidence values predicting corresponding
likelihoods that the corner points of the given anchor shape define
a given entrance to a corresponding parking spot in the field of
view of the at least one sensor.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims the benefit of U.S. Provisional
Application No. 62/819,544, filed on Mar. 16, 2019, which is hereby
incorporated by reference in its entirety.
BACKGROUND
[0002] Accurate and efficient image processing (e.g., for
recognition and classification) by a machine (e.g., a computer
programmed with a trained neural network) is important in various
contexts. For example, autonomous vehicles (e.g., vehicles equipped
with advanced driver assistance systems (ADAS)) or drones may
analyze image data in real time (e.g., representing images of a
roadway and/or a parking lot captured by a camera) to formulate
driving operations (e.g., turn steering device left, activate brake
system, etc.). In one such instance, a vehicle may analyze image
data when performing a parking operation in order to detect parking
spaces, and to identify properties of the parking spaces, such as
location, size, and orientation. To facilitate this process the
vehicle may include an object detector that is implemented using a
convolutional neural network (CNN) to detect the existence of
parking spaces in images.
[0003] A conventional CNN used to detect parking spaces may use
axis-aligned rectangular anchor boxes (all four angles are right
angles) as a form of detection output. However, parking spaces
present in sensor data are often not rectangular or axis-aligned
due to the perspective projection of the sensor. As such,
additional processing is necessary to accurately identify the
bounds of each of the parking spaces among the sensor data once
they are detected. For example, a camera on a vehicle may capture
an image of a parking space, and based on the perspective of the
camera's field of view, the parking space may not be depicted in
the image as an axis-aligned rectangle. A conventional CNN may
provide an axis-aligned rectangular anchor box as a form of
detection output, in which case additional processing is necessary
to accurately delineate the parking space in the image. When
training the conventional CNN, positive samples may be identified
using an Intersection of Union (IoU) between an anchor box output
from the CNN and a ground truth output. The IoU calculation may be
straightforward as the anchor box outputs and ground truth are both
axis-aligned rectangles.
SUMMARY
[0004] The present disclosure relates to object detection using
skewed polygons (e.g., quadrilaterals) suitable for parking space
detection. For example, in some instances at least one
Convolutional Neural Network (CNN) may be used to detect and/or
delineate one or more parking spaces represented in image data. The
CNN(s) output may be post-processed and provided to a downstream
system (e.g., vehicle control module) to inform subsequent
operations.
[0005] Aspects of the disclosure may use a CNN(s) to determine
corner points of a skewed polygon (e.g., as displacement or offset
values to anchor shape corner points) that accurately delineate a
region in an image that defines a parking space. Furthermore, the
disclosure provides for a CNN(s) that outputs confidence values
predicting likelihoods that corner points of an anchor shape define
or otherwise correspond to an entrance to a parking spot. The
confidence values may be used to select a subset of the corner
points of the anchor shape and/or skewed polygon in order to define
the entrance to the parking spot. In accordance with embodiments of
the disclosure, the CNN(s) may be used to both predict likelihoods
particular corner points of an anchor shape correspond to an
entrance to a parking space along with predicting the displacement
values to the corner points that delineate the bounds of the
parking space.
[0006] The disclosure further provides for computing a distance
(e.g., minimum aggregate distance) between corner points of a
skewed polygon predicted using a CNN(s) and ground truth corner
points of a parking spot to determine whether the anchor shape
should be used as a positive sample for training. For example, a
positive sample may be identified based at least in part on the
distance being below a threshold value.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The present systems and methods for object detection using
skewed polygons suitable for parking space detection is described
in detail below with reference to the attached drawing figures,
which are incorporated herein by reference, wherein:
[0008] FIG. 1 is an illustration including an example object
detection system, in accordance with some embodiments of the
present disclosure;
[0009] FIG. 2 is a flow diagram illustrating an example process for
identifying one or more parking spaces, in accordance with some
embodiments of the present disclosure;
[0010] FIG. 3 is an illustration of an image that may be
represented by image data processed by an object detector, a grid
of spatial elements of the object detector, and a set of anchor
shapes that may be associated with one or more of the spatial
elements, in accordance with some embodiments of the present
disclosure;
[0011] FIG. 4 is an illustration of images overlaid with visual
elements for different spatial element resolutions, in accordance
with some embodiments of the present disclosure;
[0012] FIG. 5A is an illustration that includes a neural network
for detecting parking spaces, in accordance with some embodiments
of the present disclosure;
[0013] FIG. 5B is an illustration of an image with an entry-line
delineation and a parking space delineation, in accordance with
some embodiments of the present disclosure;
[0014] FIG. 6 is an illustration of an image with ground truth data
and corner points of a skewed quadrilateral use for training of an
object detector, in accordance with some embodiments of the present
disclosure;
[0015] FIG. 7 is a block diagram illustrating a method of training
a machine learning model to provide corner points of parking
spaces, in accordance with some embodiments of the present
disclosure;
[0016] FIG. 8 is a block diagram illustrating a method for
determining, using a neural network, corner points of a parking
space, in accordance with some embodiments of the present
disclosure;
[0017] FIG. 9 is a block diagram illustrating a method of
determining, using a neural network, an entrance to a parking
space, in accordance with some embodiments of the present
disclosure;
[0018] FIG. 10 is an illustration of an example operating
environment suitable for use in implementing some embodiments of
the present disclosure;
[0019] FIG. 11A is an illustration of an example autonomous
vehicle, in accordance with some embodiments of the present
disclosure;
[0020] FIG. 11B is an example of camera locations and fields of
view for the example autonomous vehicle of FIG. 11A, in accordance
with some embodiments of the present disclosure;
[0021] FIG. 11C is a block diagram of an example system
architecture for the example autonomous vehicle of FIG. 11A, in
accordance with some embodiments of the present disclosure;
[0022] FIG. 11D is a system diagram for communication between
cloud-based server(s) and the example autonomous vehicle of FIG.
11A, in accordance with some embodiments of the present disclosure;
and
[0023] FIG. 12 is a block diagram of an example computing device
suitable for use in implementing some embodiments of the present
disclosure.
DETAILED DESCRIPTION
[0024] The present disclosure relates to object detection using
skewed polygons (e.g., quadrilaterals) suitable for parking space
detection. Disclosed approaches may be suitable for driving
operations (e.g., autonomous driving, advanced driver assistance
systems (ADAS), etc.) in which a parking space is detected, as well
as other applications (e.g., robotics, video analysis, weather
forecasting, medical imaging, etc.) detecting objects (e.g.,
buildings, windows, doors, driveways, intersections, teeth,
real-property tracts, areas or regions of surfaces, etc.)
corresponding with skewed polygons in image and/or sensor data.
[0025] The present disclosure may be described with respect to an
example autonomous vehicle 1100 (alternatively referred to herein
as "vehicle 1100" or "autonomous vehicle 1100"), an example of
which is described in more detail herein with respect to FIGS.
11A-11D. Although the present disclosure primarily provides
examples using autonomous vehicles, other types of devices may be
used to implement the various approaches described herein, such as
robots, unmanned aerial vehicles, camera systems, weather
forecasting devices, medical imaging devices, etc. In addition,
these approaches may be used for controlling autonomous vehicles,
or for other purposes, such as, without limitation, video
surveillance, video or image editing, parking space occupancy
monitoring, identification, and/or detection, video or image search
or retrieval, object tracking, weather forecasting (e.g., using
RADAR data), and/or medical imaging (e.g., using ultrasound or
magnetic resonance imaging (MRI) data).
[0026] While parking spaces are primarily described as the objects
being detected, disclosed approaches may generally apply to objects
that may appear as skewed polygons (such as quadrilaterals or other
shapes) in a field of view of a sensor and/or in image data (e.g.,
these objects may be rectangular in the real world but appear as
skewed quadrilaterals due to perspective). While disclosed
approaches are described using skewed quadrilaterals and four
corner points, disclosed concepts may apply to any number of shapes
and points (e.g., corner points) that define those shapes.
Additionally, while an entrance is primarily defined herein as
being defined by two of the points (e.g., corner points), in other
examples an entrance may be defined using any number of points
(e.g., corner points). Further, while the disclosure focuses on
object detectors implemented using neural networks, in some
embodiments other types of machine learning models may be
employed.
[0027] In contrast to conventional approaches, which may use a CNN
to predict an axis-aligned rectangular anchor box generally
indicating the size and location of a parking space, aspects of the
disclosure may use a CNN(s) to determine corner points of a skewed
quadrilateral (e.g., as displacement or offset values to anchor box
corner points) that accurately delineate a region in an image that
defines a parking space. As such, in some embodiments, the skewed
quadrilateral may be directly consumed by downstream systems
without requiring additional or significant processing to identify
the bounds of the parking space. By reducing subsequent processing,
disclosed approaches may be more efficient and faster than
conventional approaches.
[0028] Furthermore, in contrast to conventional approaches, the
disclosure provides for a CNN(s) that outputs confidence values
predicting likelihoods that corner points of an anchor box define
or otherwise correspond to an entrance to a parking spot. The
confidence values may be used to select a subset of the corner
points of the anchor box and/or skewed quadrilateral in order to
define the entrance to the parking spot. In accordance with
embodiments of the disclosure, processing may further be reduced by
using the CNN(s) to both predict likelihoods particular corner
points of an anchor box correspond to an entrance to a parking
space along with predicting the displacement values to the corner
points that delineate the bounds of the parking space.
[0029] In another aspect, while a conventional CNN uses
Intersection over Union (IoU) to determine whether an axis-aligned
rectangular anchor box output is a positive sample, the disclosure
provides for computing a minimum aggregate distance between corner
points of a skewed quadrilateral predicted using a CNN(s) and
ground truth corner points of a parking spot to determine whether
the anchor box should be used as a positive sample for training.
For example, a positive sample may be identified based at least in
part on the minimum aggregate distance (e.g., after normalization)
being below a threshold value. Computing the minimum aggregate
distance may be more straightforward than computing an IoU for a
skewed quadrilateral, resulting is reduced processing time.
Example Parking Space Detector
[0030] Now referring to FIG. 1, FIG. 1 shows an illustration
including an example object detection system 100, in accordance
with some embodiments of the present disclosure. It should be
understood that this and other arrangements described herein are
set forth only as examples. Other arrangements and elements (e.g.,
machines, interfaces, functions, orders, and groupings of
functions, etc.) may be used in addition to or instead of those
shown, and some elements may be omitted altogether for the sake of
clarity. Further, many of the elements described herein are
functional entities that may be implemented as discrete or
distributed components or in conjunction with other components, and
in any suitable combination and location. Various functions
described herein as being performed by one or more entities may be
carried out by hardware, firmware, and/or software. For instance,
some functions may be carried out by a processor executing
instructions stored in memory.
[0031] In one or more embodiments, the object detection system 100
includes, for example, a communications manager 104, an object
detector 106, a feature determiner 108, a confidence score
generator 110, a displacement value generator 112, a skewed
quadrilateral generator 114, and an entrance determiner 126. Some
examples described in this disclosure use quadrilaterals (e.g.,
regular, skewed, irregular, boxes, etc.), and the systems and
methods described may similarly use other polygons.
[0032] The communications manager 104 may be configured to manage
communications received by the object detection system 100 (e.g.,
comprising sensor data and/or image data) and/or provided by the
object detection system 100 (e.g., comprising confidence scores,
displacement scores, corner points of a skewed quadrilateral,
and/or information derived therefrom). Additionally or
alternatively, the communications manager 104 may manage
communications within the object detection system 100, such as
between any of the object detector 106, the Confidence score
generator 110, the displacement value generator 112, the skewed
quadrilateral generator 114, the entrance determiner 126, and/or
other components that may be included in the object detection
system 100 or may communicate with the object detection system 100,
(e.g., downstream system components consuming output from the
object detection system 100).
[0033] With reference to FIG. 2, FIG. 2 is a flow diagram
illustrating an example process 200 for identifying one or more
parking spaces, in accordance with some embodiments of the present
disclosure. The object detector 106 may be configured to analyze
input data, such as sensor data and/or image data representative of
any number of parking spaces (or no parking spaces), received from
the communications manager 104 and generate object detection data
that is representative of any number of detected objects captured
in the input data. To do so, the object detector 106 may use the
feature determiner 108, the displacement value generator 112, and
the confidence score generator 110. The feature determiner 108 may
be configured to generate or determine features of the input data
as inputs to the confidence score generator 110 and the
displacement value generator 112. The confidence score generator
110 may be configured to generate or determine a confidence score
118 of one or more anchor boxes based on data from the feature
determiner 108. The confidence score 118 of each anchor box may
predict a likelihood that the respective anchor box corresponds to
a parking space detected in the input data.
[0034] The displacement value generator 112 may be configured to
generate or determine displacement values 122 to corner points of
each anchor box based on data from the feature determiner 108. The
skewed quadrilateral generator 114 may receive as input, any of the
various outputs from the object detector 106, such as the
confidence value 118 and the displacement values 122 of each anchor
box. The skewed quadrilateral generator 114 may generate and/or
determine a skewed quadrilateral from the input using any suitable
technique, such as Non-Maximum Suppression (NMS). This may include
the skewed quadrilateral generator 144 determining, from any number
of anchor boxes corner points of the skewed quadrilateral from the
displacement values 122 (e.g., provided by the displacement value
generator 112) and the corner points of the anchor box(s). As a
non-limiting example, the skewed quadrilateral generator 114 may
determine which anchor boxes have a confidence value 118 exceeding
a threshold value (if any). From those anchor boxes, the skewed
quadrilateral generator 114 may filter and/or cluster the candidate
detections into one or more output object detections and determine
corner points of skewed quadrilaterals that correspond to those
output object detections (e.g., using corresponding displacement
values 122).
[0035] In addition to or instead of the confidence score generator
110 generating or determining a confidence score 118 predicting a
likelihood that a respective anchor box corresponds to a parking
space detected in the input data, the confidence score generator
110 may generate or determine a confidence score 116 predicting a
likelihood that a respective corner point(s) corresponds to a
detected entrance to a parking space represented in the input data.
The entrance determiner 126 may use at least the confidence scores
116 to determine one or more entrances to one or more parking
spaces. As a non-limiting example, the entrance determiner 126 may
define an entrance for each object detection output by the skewed
quadrilateral generator 114 by selecting a set of corner points of
each skewed quadrilateral (e.g., two corner points) that have the
highest confidence values 116 (e.g., optionally requiring those
confidence values 116 to exceed a threshold value). The selected
corner points may then be used to define an entrance to the
corresponding parking space (e.g., an entry-line that connects the
selected corner points). As indicated by a dashed line in FIG. 2,
in other examples the skewed quadrilateral generator 114 may not be
implemented in an object detection system 100 with the entrance
determiner 126 and/or used by the entrance determiner 126 in order
to identify and/or define entrances to parking spaces or other
detected object regions.
[0036] The object detection system 100 may be implemented in an
example operating environment 1000 of FIG. 10, in accordance with
some embodiments of the present disclosure. For example, the
components of FIG. 1 may generally be implemented using any
combination of a client device(s) 1020, a server device(s) 1060, or
a data store(s) 1050. Thus, the object detection system 100 may be
provided via multiple devices arranged in a distributed environment
that collectively provide the functionality described herein, or
may be embodied on a single device (e.g., the vehicle 1100). Thus,
while some examples used to describe the object detection system
100 may refer to particular devices and/or configurations, it is
contemplated that those examples may be more generally applicable
to any of the potential combinations of devices and configurations
described herein. For example, in some embodiments, at least some
of the sensors 1080 used to generate one or more portions of sensor
data input to the object detector 106 may be distributed amongst
multiple vehicles and/or objects in the environment and/or at least
one of the sensors 1080 may be included in the vehicle 1100.
[0037] As mentioned herein, the communications manager 104 may be
configured to manage communications received by the object
detection system 100 (e.g., comprising sensor data and/or image
data) and/or provided by the object detection system 100 (e.g.,
comprising the confidence scores or values, displacement values,
corner points to skewed quadrilaterals, and/or information derived
therefrom). Additionally or alternatively, the communications
manager 104 may manage communications within the object detection
system 100.
[0038] Where a communication is received and/or provided as a
network communication, the communications manager 104 may comprise
a network interface which may use one or more wireless antenna(s)
(wireless antenna(s) 1126 of FIG. 11A) and/or modem(s) to
communicate over one or more networks. For example, the network
interface may be capable of communication over Long-Term Evolution
(LTE), Wideband Code-Division Multiple Access (WCDMA), Universal
Mobile Telecommunications Service (UMTS), Global System for Mobile
communications (GSM), CDMA2000, etc. The network interface may also
enable communication between objects in the environment (e.g.,
vehicles, mobile devices, etc.), using local area network(s), such
as Bluetooth, Bluetooth Low Energy (LE), Z-Wave, ZigBee, etc.,
and/or Low Power Wide-Area Network(s) (LPWANs), such as Long Range
Wide-Area Network (LoRaWAN), SigFox, etc. However, the
communications manager 104 need not include a network interface,
such as where the object detection system 100 implemented
completely on an autonomous vehicle (e.g., the vehicle 1100). In
some examples, one or more of the communications described herein
may be between components of a computing device 1200 over a bus
1202 of FIG. 12.
[0039] Sensor data received by the communications manager 104 may
be generated using any combination of the sensors 1080 of FIG. 10.
For example, the sensor data may include image data representing an
image(s), image data representing a video (e.g., snapshots of
video), and/or sensor data representing fields of view of sensors
(e.g., LIDAR data from LIDAR sensor(s) 1164, RADAR data from RADAR
sensor(s) 1160, image data from a camera(s) of FIG. 11B, etc.).
[0040] The sensor data and/or image data that the communications
manager 104 provides to the object detector 106 may be generated in
a physical or virtual environment and may include image data
representative of a field(s) of view of a camera(s). For example,
in aspects of the present disclosure, the communications manager
104 provides to the object detector 106 image data generated by a
camera of the vehicle 1100 in a physical environment.
[0041] While some examples of a machine learning model(s) that may
be used for the object detector 106 and/or other components
described herein may refer to specific types of machine learning
models (e.g., neural networks), it is contemplated that examples of
the machine learning models described herein may, for example and
without limitation, include any type of machine learning model,
such as a machine learning model(s) using linear regression,
logistic regression, decision trees, support vector machines (SVM),
Naive Bayes, k-nearest neighbor (Knn), K means clustering, random
forest, dimensionality reduction algorithms, gradient boosting
algorithms, neural networks (e.g., auto-encoders, convolutional,
recurrent, perceptrons, Long/Short Term Memory (LSTM), Hopfield,
Boltzmann, deep belief, deconvolutional, generative adversarial,
liquid state machine, etc.), and/or other types of machine learning
models.
[0042] Referring to FIG. 3, FIG. 3 is an illustration of an image
that may be represented by image data processed by an object
detector, a grid of spatial elements of the object detector, and a
set of anchor boxes that may be associated with one or more of the
spatial elements, in accordance with some embodiments of the
present disclosure. For example, FIG. 3 includes a depiction of an
image 310 that may be generated by a camera of the vehicle 1100 in
the physical environment and provided to the object detector 106,
which may analyze the image data to generate object detection data.
The object detection data may be representative of detections, by
the object detector 106, of objects in the image 310 (which may
also be referred to as detected objects). The detected objects may
or may not correspond to actual objects depicted in the image 310.
For example, some of the detected objects may correspond to false
detections made by the object detector 106. Further, some of the
detected objects may correspond to the same object depicted in the
image 310.
[0043] The object detector 106 may comprise one or more machine
learning models trained to generate the object detection data from
features extracted from the sensor data (e.g., the image data). In
some examples, the object detector 106 is configured to determine a
set of object detection data (e.g., representing a confidence value
and displacement values to corner points) for each spatial element
and/or one or more corresponding anchor boxes thereof for a field
of view and/or image. In various examples, a spatial element may
also refer to a grid cell, an output cell, a super-pixel, and/or an
output pixel of the object detector 106.
[0044] In various examples, the spatial elements may form a grid of
spatial element regions. For example, FIG. 3 visually indicates a
grid 312 of spatial elements of the object detector 106 that may be
logically applied to sensor data (e.g., representing the image
310). In FIG. 3, the grid 312 is depicted separately from the image
310 so as not to obscure the image 310, and an overlaid depiction
402 is provided in FIG. 4. The spatial elements, such as a grid
cell 311, may be defined by a location in the grid. For example,
each grid-cell may contain a spatial element region of a spatial
element. In other examples, grid-based spatial elements may not be
used. Further, the spatial elements may not necessarily define
contiguous spatial element regions, may not necessarily define
rectangular-shaped spatial element regions, and/or may not cover
all regions of a field of view and/or image.
[0045] In some examples, for a single image or frame (e.g., the
image 310), or a set of images or frames, each spatial element of
the object detector 106 may provide the object detection data for
one or more corresponding anchor boxes. In other examples, one or
more spatial elements may not provide object detection data. The
object detection data may be representative of, for example, the
confidence value 118, the displacement values 122, and/or the
confidence values 116 of each anchor box of the spatial element,
which may or may not correspond to a parking space in the field of
view and/or the image 310.
[0046] FIG. 3 illustrates a set of anchor boxes 314 where each
spatial element applied to the image 310 may be associated with a
corresponding set of the anchor boxes 314. Illustrated are eight
anchor boxes, but any number of anchor boxes may be used for a
spatial element and anchor boxes for different spatial elements may
be different from one another in shape, size, number, etc. The
anchor boxes may be various sizes and shapes, such as regular
rectangles (e.g., equiangular rectangles); and in contrast to some
conventional systems, the anchor boxes may also include one or more
skewed quadrilaterals, such as irregular rectangles (e.g., no
congruent angles); rhombus; kite; trapezoid; parallelogram;
isosceles trapezoid; skewed quadrilateral; or any combination
thereof. In FIG. 3, the anchor boxes 314 are depicted separate from
the image 310 so as not to obscure the image 310, and an overlaid
depiction 402 is provided in FIG. 4.
[0047] As described herein, FIG. 4 provides the overlaid depiction
402 in which the image 310 is overlaid with the grid 312, and the
anchor boxes 314 for a single spatial element are positioned at the
grid cell 311 it indicate corresponding locations with respect to
the image 310. As described herein, a confidence score(s) and
displacement values may be generated for each anchor box of each
grid cell and/or spatial element. For example purposes, the anchor
boxes 314 are depicted for only one grid cell 311, and in other
aspects the anchor boxes 314 (or a variation thereof) may be used
for multiple grid cells of the grid 312, or each grid cell of the
grid 312. The anchor boxes 314 for a different grid cell 311 may be
at locations corresponding to that grid cell 311 (or more generally
spatial element). The grid 312 is an example of one size or
resolution of spatial element. As a non-limiting example, the grid
312 is 10.times.6 with 60 grid cells, and as such, if each grid
cell is associated with eight anchor boxes, a confidence score(s)
and displacement values may be generated for 480 different anchor
boxes.
[0048] In other aspects, a grid or other arrangement of spatial
element regions may have a different size or resolution with more
spatial regions or fewer spatial regions, in which case the scale
of the anchor boxes may be increased (e.g., with a courser grid
with fewer, larger spatial regions) or decreased (e.g., with a
finer grid with more, smaller spatial regions). For example, the
overlaid depiction 404 includes the image 310 overlaid with a
courser resolution grid 412 (e.g., 2.times.2) and with a different
set of anchor boxes 414, which may be congruent to anchor boxes 314
(e.g., same shape and size), similar to anchor boxes 314 (e.g.,
same shape and/or different size), or dissimilar to anchor boxes
314 (e.g., different shape and/or different size). In some aspects
of the present disclosure, the object detector may apply multiple
resolutions of spatial element regions (e.g., grids) to the same
input data, each spatial element region corresponding to a
respective set of anchor boxes. Among other potential advantages,
using multiple resolutions may improve the likelihood that the
object detector 106 is accurate for both larger parking spaces and
smaller parking spaces, whether in the same image (e.g., parking
spaces closer to the camera may appear larger based on the
perspective, and parking spaces farther from the camera may appear
smaller) or different images. In some instances, the actual sets of
spatial element regions (e.g., grids) used to analyze input data
may be significantly finer in resolution than the grids 312 and
412. Further, any number of sets of spatial element regions may be
employed.
[0049] As described herein, based on the object detection data
provided by the object detector 106 the skewed quadrilateral
generator 114 may generate and/or identify one or more skewed
quadrilaterals corresponding to one or more parking spaces and the
entrance determiner 126 may determine and/or identify one or more
entrances to one or more parking spaces.
[0050] Referring to FIG. 5A, FIG. 5A depicts at least a portion of
an example object detector 106 implemented using a neural
network(s) (e.g., a CNN). For example, the object detector 106
includes a feature backbone network 506, such as ResNet 50 or
another feature backbone network. In addition, the neural network
includes a feature pyramid network 508. Furthermore, the neural
network includes a classification sub-network 510.
[0051] In embodiments, the feature backbone network 506 and the
feature pyramid network 508 may correspond to the feature
determiner 108 of FIG. 1, the classification sub-network 510 may
correspond to the confidence score generator 110 of FIG. 1, and the
regression sub-network 512 may correspond to the displacement value
generator 112 of FIG. 1. However, the depiction of the neural
network in FIG. 5A is not intended to limit the object detector 106
to the neural network shown. Additionally, the classification
sub-network 510 is shown as outputting data representative of a
confidence score 514 (which may correspond to the confidence score
118 in FIG. 2). Although not shown for simplicity, in embodiments
that detect entrances to parking spaces, the classification
sub-network 510 may additionally or alternatively output data
representative of the confidence scores 116 of FIG. 1 or another
classification sub-network may be used. The regression sub-network
512 is shown as outputting data representative of displacement
values 516 (which may correspond to the displacement values 122 in
FIG. 2). The outputs described with respect to the object detector
106 in FIG. 5A may be provided for each pre-defined anchor box.
[0052] In a further aspect of the present disclosure, a skewed
quadrilateral generator 518, which may correspond to the skewed
quadrilateral generator 114 in FIG. 1, may generate and/or identify
one or more skewed quadrilaterals based on the outputs from the
object detector 106. For example, based on the displacement values
516 (e.g., .DELTA.x.sub.1, .DELTA.y.sub.1 . . . , .DELTA.x.sub.4,
.DELTA.y.sub.4) and the confidence value 514, the skewed
quadrilateral generator 518 may select the anchor box and adjust
the corner positions or points of the anchor box 522 (e.g.,
x.sub.1, y.sub.1 . . . , x.sub.4, y.sub.4), to generate corner
points (e.g., adjusted corner points 520 including [x'.sub.1,
y'.sub.1 . . . , x'.sub.4, y'.sub.4]) of a skewed quadrilateral.
Data representative of the skewed quadrilateral (e.g., the adjusted
corner points 520) may be provided to various downstream components
or systems. As shown, in various embodiments, confidence map
classification may be performed, such as to classify the anchor box
as a positive or negative parking space detection (e.g., using a
binary classification) and the skewed quadrilateral generator 518
may leverage this information. For example, the object detection
system 100 may compare the confidence value 514 of each anchor box
to a threshold value. A positive detection may result for an anchor
box when the confidence value 514 is greater than the threshold
value and a negative detection may result when the confidence value
is less than the threshold value.
[0053] As non-limiting examples, the skewed quadrilateral generator
114 may generate and/or determine any number of skewed
quadrilaterals by forming any number of clusters of detected
objects by applying a clustering algorithm(s) to the outputs of the
object detector 106 for the detected objects (e.g., after filtering
out negative detections using the confidence values 514). To
cluster detected objects, the skewed quadrilateral generator 114
may cluster the locations of the detected objects (e.g., candidate
skewed quadrilaterals) together. This may be, for example, based at
least in part on the confidence values 514 associated with the
detected objects and/or other detected object data described
herein. In some examples, the skewed quadrilateral generator 114
uses a Density-Based Spatial Clustering of Applications with Noise
(DBSCAN) algorithm. Other examples include NMS or modified group
Rectangles algorithms. A skewed quadrilateral may be selected,
determined, and/or generated from each cluster as an output object
detection (e.g., using one or more algorithms and/or neural
networks).
[0054] Data representative of the adjusted corner points 520 and/or
each skewed quadrilateral determined by the skewed quadrilateral
generator 114, may be provided to various downstream components or
systems. For example, in one instance, the corner points of skewed
quadrilaterals may be provided to a vehicle control module, which
may directly consume the corner points by converting the
two-dimensional corner point coordinates to three-dimensional
coordinates or otherwise processing that data, such as to
coordinate parking operations of a vehicle. In another aspect, the
corner points of a skewed quadrilateral(s) may be provided to an
instrument cluster control module having a video or image monitor
for displaying a representation of the one or more parking spaces.
For example, the corner points may be used to annotate the image
502 and/or an image corresponding or the image 502 with the corner
points delineated--e.g., annotated image 525 in FIG. 5A with the
delineation (e.g., indicated by dotted lines) 526 of skewed
quadrilaterals having adjusted corner points 520.
[0055] In a further aspect, the corner points of the skewed
quadrilateral, the displacement values 122, and/or confidence
values corresponding to the corner points of the anchor box (e.g.,
the confidence values 116) may be provided as input to the entrance
determiner 126 to detect and/or define one or more entrances to one
or more parking spaces (e.g., parking spaces identified by the
skewed quadrilateral generator 114). For example, an entry line can
be detected and/or defined by selecting the two corner points
(e.g., of the four) with the highest confidence values amongst the
confidence values 116 of an anchor box. In some examples, the
selection may further be based on the confidence values being
greater than a threshold value (e.g., indicating the corner points
are each likely to correspond to an entrance). The entrance to a
parking space may be defined as the entry line or otherwise
determined and/or defined using the locations of the selected
corner points.
[0056] As such, the entrance information determined by the entrance
determiner 126 may be provided to various downstream components or
systems. For example, in some instances, the corner points
identified as corresponding to an entrance may be provided to a
vehicle control module, which may directly consume the corner
points by converting the two-dimensional corner point coordinates
to three-dimensional coordinates or otherwise processing the corner
points. In another aspect, the corner points may be provided to an
instrument cluster control module having a video or image monitor
for displaying a representation of the one or more entrances to one
or more parking spaces. For example, the corner points may be used
to annotate the image 502 and/or an image corresponding or the
image 502 with the corner points and/or entrance delineated--e.g.,
annotated image 530 in FIG. 5B may include the delineation (e.g.,
indicated by dashed lines) 532 of an entry line to a parking space.
Optionally a parking space(s) delineation 534 (e.g., dotted line)
may also be provided. In one example, a delineation of an entrance
and/or a parking space may include a colored-line or other suitable
annotation to an image.
Examples of Training a Machine Learning Model(s) for Object
Detection
[0057] The object detector 106 may be trained using various
possible approaches. In some examples, the object detector 106 may
be trained in a fully supervised manner. Training images together
with their labels may be grouped in minibatches, where the size of
the minibatches may be a tunable hyperparameter. Each minibatch may
be passed to an online data augmentation layer which may apply
transformations to images in that minibatch. The data augmentation
may be used to alleviate possible overfitting of the object
detector 106 to the training data. The data augmentation
transformations may include (but are not limited to) spatial
transformations such as left-right flipping, zooming-in/-out,
random translations, etc., color transformations such as hue,
saturation and contrast adjustment, or additive noise. Labels may
be transformed to reflect corresponding transformations made to
training images.
[0058] Augmented images may be passed to the object detector 106 to
perform forward pass computations. The object detector 106 may
perform feature extraction and prediction on a per spatial element
basis (e.g., predictions related to anchor boxes). Loss functions
may simultaneously measure the error in the tasks of predicting the
various outputs (e.g., the confidence values and the displacement
values for each anchor box).
[0059] The component losses for the various outputs may be combined
together in a single loss function that applies to the whole
minibatch. Then, backward pass computations may take place to
recursively compute gradients of the cost function with respect to
trainable parameters (typically at least the weights and biases of
the object detector 106, but not limited to this as there may be
other trainable parameters, e.g. when batch normalization is used).
Forward and backward pass computations may typically be handled by
a deep learning framework and software stack underneath.
[0060] A parameter update for the object detector 106 may then take
place. An optimizer may be used to make an adjustment to trainable
parameters. Examples include stochastic gradient descent, or
stochastic gradient descent with a momentum term. The main
hyperparameter connected to the optimizer may be the learning rate.
There may also be other hyperparameters depending on the
optimizer.
[0061] Images in the dataset may be presented in a random order for
each epoch during training, which may lead to faster convergence.
An epoch may refer to the number of forward/backward pass
iterations used to show each image of the dataset once to the
object detector 106 under training. The whole process
`forward-pass--backward-pass--parameter update` may be iterated
until convergence of the trained parameters. Convergence may be
assessed by observing the value of the loss function decrease to a
sufficiently low value on both the training and validation sets,
and determining that iterating further would not decrease the loss
any further. Other metrics could be used to assess convergence,
such as average precision computed over a validation set.
[0062] During training, validation may be performed periodically,
and this may involve checking the average values of the loss
function over images in a validation set (separate from the
training set). As mentioned herein, each of the outputs of the
object detector 106 (e.g., confidence score(s) of each anchor box,
displacement values of each anchor box, etc.) may be associated
with a separate loss function used for training. Any suitable loss
function(s) may be used.
[0063] In accordance with an aspect of the present disclosure,
ground-truth data for a parking space may include corner locations
of the parking space, and the corner locations may form or define a
skewed quadrilateral. Furthermore, positive training samples may be
identified from the outputs of the object detector 106 when skewed
quadrilateral corners of anchor boxes are similar enough to the
ground-truth corner locations, such as based on matching costs
being less than a threshold. In an aspect of the present
disclosure, various types of anchor boxes may be used to train the
neural network and identify positive samples. For example, in one
aspect, the predefined anchor boxes may include rectangles (e.g.,
rectangles). Further, the predefined anchor boxes may include
rotated rectangles. In addition or instead, one or more of the
anchor boxes may include skewed and rotated rectangles. Examples of
skewed rectangles include irregular rectangles (e.g., no congruent
angles); rhombus; kite; trapezoid; parallelogram; isosceles
trapezoid; skewed quadrilateral; and any combination thereof.
Predefined anchor boxes may be manually designed or obtained from
ground-truth labeling and may be used to compute ground-truth
displacement values used to train the object detector 106. An
anchor box obtained from ground-truth labeling may be referred to
as a "data-driven anchor box," which is generated by clustering or
otherwise analyzing ground-truth samples. For example, ground-truth
samples (e.g., including skewed quadrilaterals) may be generated
for one or more images. The ground-truth samples may then be
clustered into one or more clusters, and at least one data-driven
anchor box may be generated, selected, and/or determined from the
samples of each cluster of the one or more clusters. In some
examples, a data-driven anchor box may have a shape computed from
one or more of the samples of the cluster (e.g., corresponding to
an average or otherwise statistically derived shape of the
cluster). In various examples, spectral clustering may be executed,
such as by computing the affinity matrix of ground-truth samples
using a shape similarity function, and performing spectral
clustering using the affinity matrix with k clusters where k is the
number of clusters to be generated.
[0064] In one aspect the matching cost used to identify a positive
sample from output of the object detector 106 is based at least in
part on a minimum aggregate distance between the predefined
anchor-box corners as adjusted by the corresponding displacement
values that are output by the object detector 106 and the
ground-truth corner locations. This is in contrast to determining
positive samples based on intersection of union (IOU) and may be
more straightforward than IOU, since the corner points being
compared may not define regular rectangles (and instead define
skewed quadrilaterals).
[0065] A minimum aggregate distance may be computed in various
manners. For example, referring to FIG. 6, an image 610 is depicted
in which ground-truth corner points (B1, B2, B3, and B4) of a
depicted parking space 602 are shown. The image 610 may be used as
a training input to the object detector 106. As a result, the
object detector 106 may provide displacement values to corner
points of an anchor box that are used to compute adjusted corner
points (A1, A2, A3, and A4) of the anchor box, as shown. FIG. 6
shows corner points for only a single anchor box to simplify this
illustration, and in other aspects, similar information may be used
for each anchor box described herein.
[0066] In one aspect of the present disclosure, computing a minimum
aggregate distance includes computing a minimum mean distance. For
example, a first aggregate distance may be computed by determining
distances between (A1, B1), (A2, B2), (A3, B3), and (A4, B4), then
statistically deriving the first aggregate distance from those
distances, such as using a mean. A second, third, and fourth
aggregate distance may also be computed by changing the
associations between the corner points of each data set (e.g., for
each possible combination)--e.g., a second aggregate distance using
(A1, B2), (A2, B3), (A3, B4), and (A4, B1); a third aggregate
distance using (A1, B3), (A2, B4), (A3, B1), and (A4, B2); and a
fourth aggregate distance using (A1, B4), (A2, B1), (A3, B2), and
(A4, B3). A minimum aggregate distance may then be selected from
among the various aggregate distances, and used to determine
whether the anchor box is a positive training sample (e.g., similar
to an IOU). For example, a positive sample may be selected based at
least in part on the mean aggregate distance being less than a
threshold value. In other aspects, an average mean distance, or
other statistical quantification may be selected and used to
determine whether a matching cost is less than a threshold.
[0067] In some aspects of the disclosure, the minimum aggregate
distance may be determined for any number of anchor boxes
associated with the object detector 106, to determine whether the
anchor box corresponds to a positive sample for training. The
confidence values 118 may be used to filter anchor boxes from
consideration as being a positive sample. For example, the minimum
aggregate distance may be determined for an anchor box based at
least in part on a confidence value 118 that is associated with
that anchor box. In some examples, the minimum aggregate distance
may be determined for each anchor box having a confidence value 118
that exceeds a threshold value (e.g., indicating a positive
detection).
[0068] In a further aspect of the disclosure, the minimum aggregate
distance for each anchor box may be normalized based at least in
part on a size and/or area defined by the ground-truth corner
points (e.g., the ground-truth skewed quadrilateral). Normalizing
the minimum aggregate distances may be used to account for size
differences between anchor boxes, such as where different anchor
box sizes and/or spatial element region (e.g., grid) resolutions
are employed. In accordance with the disclosure, an anchor box may
be identified as a positive sample when the matching cost (e.g.,
based at least in part on the normalized minimum aggregate
distance) is less than a certain (e.g., predetermined) threshold.
Positive samples may then be used to update parameters of the
object detector 106 (e.g., CNN) being trained.
[0069] Now referring to FIG. 7, FIG. 7 is a flow diagram showing a
method 700 for training a machine learning model to provide corner
points of parking spaces, in accordance with some embodiments of
the present disclosure. Each block of the method 700, and other
methods described herein, comprises a computing process that may be
performed using any combination of hardware, firmware, and/or
software. For instance, various functions may be carried out by a
processor executing instructions stored in memory. The method 700
may also be embodied as computer-usable instructions stored on
computer storage media. The method 700 may be provided by a
standalone application, a service or hosted service (standalone or
in combination with another hosted service), or a plug-in to
another product, to name a few. Methods described herein may
additionally or alternatively be executed by any one system, or any
combination of systems, including, but not limited to, those
described herein and are not limited to particular examples.
[0070] The method 700, at block B702, includes applying, to a
neural network, image data representative of a parking space. For
example, the image 502 may be applied to the object detector 106,
the image 502 depicting at least one parking space.
[0071] The method 700, at block B704, includes receiving, using the
neural network, data generated from the image data and
representative of displacement values to corner points of an anchor
shape. For example, the regression sub-network 512 may output the
displacement values 516 related to the pre-defined anchor box 522
and generated from image data representing the image 502.
[0072] The method 700, at block B706, includes determining corner
points of a skewed polygon from the displacement values to the
corner points of the anchor shape. For example, the
skewed-quadrilateral generator 518 (or other component used at
least for training) may determine the adjusted corner points 520 of
a skewed quadrilateral from the displacement values 516 related to
the pre-defined anchor box 522.
[0073] The method 700, at block B708, includes computing a first
distance between the corner points of the skewed polygon and
ground-truth corner points of the parking space. For example, a
minimum aggregate distance may be computed between (A1, A2, A3, and
A4) and (B1, B2, B3, and B4), as described with respect to FIG.
6.
[0074] The method 700, at block B710, includes determining a sample
rating based on the first distance. For example, the sample rating
may be the minimum aggregate distance or some derivative thereof
(e.g., normalized based on ground-truth size).
[0075] The method 700, at block B712, includes based on the sample
rating exceeding (e.g., being below) a threshold value, updating
parameters of the neural network using the anchor shape as a
positive training sample. For example, an anchor may be defined as
a positive sample when the matching cost (e.g., based on the sample
rating) is less than a threshold.
[0076] Now referring to FIG. 8, FIG. 8 is a flow diagram showing a
method 800 for determining, using a neural network, corner points
of a parking space, in accordance with some embodiments of the
present disclosure. The method 800, at block B802, includes
applying, to a neural network, sensor data representative of a
field of view of at least one sensor in an environment. For
example, sensor data representative of the image 502 may be applied
to the object detector 106, the image representing a field of view
of a camera of the vehicle 1100.
[0077] The method 800, at block B804, includes receiving, from the
neural network, first data and second data generated from the
sensor data, the first data representative of displacement values
to corner points of an anchor shape and the second data
representative of a confidence value predicting a likelihood that
the anchor shape corresponds to a parking space in the field of
view of the at least one sensor. For example, the regression
sub-network 512 may output data representative of the displacement
values 516 related to the pre-defined anchor box 522 and generated
from the sensor data representing the image 502. In addition, the
classification sub-network 510 may output data representative of a
confidence score 514 predicting a likelihood that the anchor box
522 corresponds to a parking space in the image 502.
[0078] The method 800, at block B806, includes based on the
confidence value exceeding a threshold value, determining corner
points of a skewed polygon from the displacement values to the
corner points of the anchor shape. For example, the
skewed-quadrilateral generator 518 may determine data
representative of the adjusted corner points 520 of a skewed
quadrilateral from the displacement values 516 related to the
pre-defined anchor box 522 based at least in part on the confidence
value 514 exceeding a threshold value, as indicated in FIG. 5A.
[0079] Now referring to FIG. 9, FIG. 9 is a flow diagram showing a
method 900 for determining, using a neural network, an entrance to
a parking space, in accordance with some embodiments of the present
disclosure. The method 900, at block B902, includes applying, to a
neural network, sensor data representative of a field of view of at
least one sensor in an environment. For example, sensor data
representative of the image 502 may be applied to the object
detector 106, the sensor data representing a field of view of a
camera of the vehicle 1100.
[0080] The method 900, at block B904, includes receiving, from the
neural network, first data and second data generated from the image
data. The first data is representative of displacement values to
corner points of an anchor shape, and the second data is
representative of confidence values predicting likelihoods that the
corner points of the anchor shape define an entrance to a parking
space in the field of view of the at least one sensor. For example,
the regression sub-network 512 may output data representative of
the displacement values 516 related to the pre-defined anchor box
522 and generated from the sensor data. In addition, the
classification sub-network 510 (or another similar network) may
output the confidence scores 116 of FIG. 2 predicting likelihood
that corner points of the anchor box represent at least a portion
of an entrance to a parking space.
[0081] The method 900, at block B906, includes selecting a subset
of the corner points of the anchor shape based on the confidence
values. For example, the entrance determiner 126 may filtered the
corner points to determine and/or select the corner points with the
highest confidence scores 116.
[0082] The method 900, at block B908, includes identifying the
entrance to the parking space from the subset of the corner points.
For example, once the two corner points with the highest confidence
scores have been selected, the entrance determiner 126 may be
designated defining an entrance and/or the entry-line for a parking
space.
Example Operating Environment
[0083] The object detection system 100 and/or the network 502 may
be implemented in an example operating environment 1000 of FIG. 10,
in accordance with some embodiments of the present disclosure.
[0084] Among other components not illustrated, the operating
environment 1000 includes a client device(s) 1020, a network(s)
1040, a server device(s) 1060, a sensor(s) 1080, and a data
store(s) 1050. It should be understood that operating environment
1000 shown in FIG. 10 is an example of one suitable operating
environment. Each of the components shown in FIG. 10 may be
implemented via any type of computing device, such as one or more
of computing device 1200 described in connection with FIG. 12, for
example. These components may communicate with each other via the
network 1040, which may be wired, wireless, or both. The network
1040 may include multiple networks, or a network of networks, but
is shown in simple form so as not to obscure aspects of the present
disclosure. By way of example, the network 1040 may include one or
more wide area networks (WANs), one or more local area networks
(LANs), one or more public networks such as the Internet, and/or
one or more private networks. Where the network 1040 includes a
wireless telecommunications network, components such as a base
station, a communications tower, or even access points (as well as
other components) may provide wireless connectivity. In any
example, at least one network 1040 may correspond to the network(s)
1190 of FIG. 11D, described further below.
[0085] It should be understood that any number of the client
devices 1020, the server devices 1060, the sensors 1080, and the
data stores 1050 may be employed within the operating environment
1000 within the scope of the present disclosure. Each may be
configured as a single device or multiple devices cooperating in a
distributed environment.
[0086] The client device(s) 1020 may include at least some of the
components, features, and functionality of the example computing
device 1200 described herein with respect to FIG. 12. By way of
example and not limitation, a client device 1020 may be embodied as
a personal computer (PC), a laptop computer, a mobile device, a
smartphone, a tablet computer, a smart watch, a wearable computer,
a personal digital assistant (PDA), an MP3 player, a global
positioning system (GPS) or device, a video player, a handheld
communications device, a gaming device or system, an entertainment
system, a vehicle computer system, an embedded system controller, a
remote control, an appliance, a consumer electronic device, a
workstation, any combination of these delineated devices, or any
other suitable device. In any example, at least one client device
1020 may be part of a vehicle, such as the vehicle 1100 of FIGS.
11A-11D, described in further detail herein.
[0087] The client device(s) 1020 may include one or more
processors, and one or more computer-readable media. The
computer-readable media may include computer-readable instructions
executable by the one or more processors. The instructions may,
when executed by the one or more processors, cause the one or more
processors to perform any combination and/or portion of the methods
described herein and/or implement any portion of the functionality
of the object detection system 100 of FIG. 1.
[0088] The server device(s) 1060 may also include one or more
processors, and one or more computer-readable media. The
computer-readable media includes computer-readable instructions
executable by the one or more processors. The instructions may,
when executed by the one or more processors, cause the one or more
processors to perform any combination and/or portion of the methods
described herein and/or implement any portion of the functionality
of the object detection system 100 of FIG. 1. In any example, at
least one server device 1060 may correspond to the server(s) 1178
of FIG. 11D, described in further detail herein.
[0089] The data store(s) 1050 may comprise one or more
computer-readable media. The computer-readable media may include
computer-readable instructions executable by the one or more
processors. The instructions may, when executed by the one or more
processors, cause the one or more processors to perform any
combination and/or portion of the methods described herein and/or
implement any portion of the functionality of the object detection
system 100 of FIG. 1. The data store(s) 1050 (or computer data
storage) is depicted as a single component, but may be embodied as
one or more data stores (e.g., databases) and may be at least
partially in the cloud. One or more of the data store(s) 1050 may
correspond to one or more of the data stores of FIG. 11C.
[0090] Although depicted external to the server device(s) 1060 and
the client device(s) 1020, the data store(s) 1050 may be at least
partially embodied on any combination of the server device(s) 1060
and/or the client device(s) 1020 (e.g., as memory 1204 (FIG. 12)).
For example, some information may be stored on a client device(s)
1020, and other and/or duplicate information may be stored
externally (e.g., on a server device(s) 1060). Thus, it should be
appreciated that information in the data store(s) 1050 may be
distributed in any suitable manner across one or more data stores
for storage (which may be hosted externally). For example, the data
store(s) 1050 may comprise at least some of the one or more
computer-readable media of the server device(s) 1060 and/or at
least some of the one or more computer-readable media of the client
device(s) 1020.
[0091] The sensor(s) 1080 comprise at least one sensor capable of
generating sensor data representative of at least some aspect of an
environment. For example, the sensor(s) 1080 may generate the
sensor data 102 of FIG. 1A. The sensor(s) 1080 may comprise any
combination of a global navigation satellite systems (GNSS)
sensor(s) (e.g., Global Positioning System (GPS) sensor(s)), RADAR
sensor(s), ultrasonic sensor(s), LIDAR sensor(s), inertial
measurement unit (IMU) sensor(s) (e.g., accelerometer(s),
gyroscope(s), magnetic compass(es), magnetometer(s), etc.),
microphone(s), stereo camera(s), wide-view camera(s) (e.g., fisheye
cameras), infrared camera(s), surround camera(s) (e.g., 360 degree
cameras), long-range and/or mid-range camera(s), speed sensor(s)
(e.g., for measuring the speed of the vehicle 1100), vibration
sensor(s), steering sensor(s), brake sensor(s) (e.g., as part of
the brake sensor system), and/or other sensor types.
[0092] With reference to FIGS. 11A-11C, the sensor data 102 may be
generated by, for example and without limitation, global navigation
satellite systems (GNSS) sensor(s) 1168 (e.g., Global Positioning
System sensor(s)), RADAR sensor(s) 1160, ultrasonic sensor(s) 1162,
LIDAR sensor(s) 1164, inertial measurement unit (IMU) sensor(s)
1166 (e.g., accelerometer(s), gyroscope(s), magnetic compass(es),
magnetometer(s), etc.), microphone(s) 1196, stereo camera(s) 1168,
wide-view camera(s) 1170 (e.g., fisheye cameras), infrared
camera(s) 1172, surround camera(s) 1174 (e.g., 360 degree cameras),
long-range and/or mid-range camera(s) 1198, speed sensor(s) 1144
(e.g., for measuring the speed of the vehicle 1100), vibration
sensor(s) 1142, steering sensor(s) 1140, brake sensor(s) (e.g., as
part of the brake sensor system 1146), and/or other sensor
types.
[0093] In some examples, the sensor data 102 may be generated by
forward-facing and/or side-facing cameras, such as a wide-view
camera(s) 1170, a surround camera(s) 1174, a stereo camera(s) 1168,
and/or a long-range or mid-range camera(s) 1198. In some examples,
more than one camera or other sensor may be used to incorporate
multiple fields of view (e.g., the field of view of the long-range
cameras 1198, the forward-facing stereo camera 1168, and/or the
forward facing wide-view camera 1170 of FIG. 11B).
[0094] Example Autonomous Vehicle
[0095] FIG. 11A is an illustration of an example autonomous vehicle
1100, in accordance with some embodiments of the present
disclosure. The autonomous vehicle 1100 (alternatively referred to
herein as the "vehicle 1100") may include a passenger vehicle, such
as a car, a truck, a bus, and/or another type of vehicle that
accommodates one or more passengers. Autonomous vehicles are
generally described in terms of automation levels, defined by the
National Highway Traffic Safety Administration (NHTSA), a division
of the US Department of Transportation, and the Society of
Automotive Engineers (SAE) "Taxonomy and Definitions for Terms
Related to Driving Automation Systems for On-Road Motor Vehicles"
(Standard No. J3016-201806, published on Jun. 11, 2018, Standard
No. J3016-201609, published on Sep. 30, 2016, and previous and
future versions of this standard). The vehicle 1100 may be capable
of functionality in accordance with one or more of Level 3-Level 5
of the autonomous driving levels. For example, the vehicle 1100 may
be capable of conditional automation (Level 3), high automation
(Level 4), and/or full automation (Level 5), depending on the
embodiment.
[0096] The vehicle 1100 may include components such as a chassis, a
vehicle body, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles,
and other components of a vehicle. The vehicle 1100 may include a
propulsion system 1150, such as an internal combustion engine,
hybrid electric power plant, an all-electric engine, and/or another
propulsion system type. The propulsion system 1150 may be connected
to a drive train of the vehicle 1100, which may include a
transmission, to enable the propulsion of the vehicle 1100. The
propulsion system 1150 may be controlled in response to receiving
signals from the throttle/accelerator 1152.
[0097] A steering system 1154, which may include a steering wheel,
may be used to steer the vehicle 1100 (e.g., along a desired path
or route) when the propulsion system 1150 is operating (e.g., when
the vehicle is in motion). The steering system 1154 may receive
signals from a steering actuator 1156. The steering wheel may be
optional for full automation (Level 5) functionality.
[0098] The brake sensor system 1146 may be used to operate the
vehicle brakes in response to receiving signals from the brake
actuators 1148 and/or brake sensors.
[0099] Controller(s) 1136, which may include one or more system on
chips (SoCs) 1104 (FIG. 11C) and/or GPU(s), may provide signals
(e.g., representative of commands) to one or more components and/or
systems of the vehicle 1100. For example, the controller(s) may
send signals to operate the vehicle brakes via one or more brake
actuators 1148, to operate the steering system 1154 via one or more
steering actuators 1156, to operate the propulsion system 1150 via
one or more throttle/accelerators 1152. The controller(s) 1136 may
include one or more onboard (e.g., integrated) computing devices
(e.g., supercomputers) that process sensor signals, and output
operation commands (e.g., signals representing commands) to enable
autonomous driving and/or to assist a human driver in driving the
vehicle 1100. The controller(s) 1136 may include a first controller
1136 for autonomous driving functions, a second controller 1136 for
functional safety functions, a third controller 1136 for artificial
intelligence functionality (e.g., computer vision), a fourth
controller 1136 for infotainment functionality, a fifth controller
1136 for redundancy in emergency conditions, and/or other
controllers. In some examples, a single controller 1136 may handle
two or more of the above functionalities, two or more controllers
1136 may handle a single functionality, and/or any combination
thereof.
[0100] The controller(s) 1136 may provide the signals for
controlling one or more components and/or systems of the vehicle
1100 in response to sensor data received from one or more sensors
(e.g., sensor inputs). The sensor data may be received from, for
example and without limitation, global navigation satellite systems
sensor(s) 1158 (e.g., Global Positioning System sensor(s)), RADAR
sensor(s) 1160, ultrasonic sensor(s) 1162, LIDAR sensor(s) 1164,
inertial measurement unit (IMU) sensor(s) 1166 (e.g.,
accelerometer(s), gyroscope(s), magnetic compass(es),
magnetometer(s), etc.), microphone(s) 1196, stereo camera(s) 1168,
wide-view camera(s) 1170 (e.g., fisheye cameras), infrared
camera(s) 1172, surround camera(s) 1174 (e.g., 360 degree cameras),
long-range and/or mid-range camera(s) 1198, speed sensor(s) 1144
(e.g., for measuring the speed of the vehicle 1100), vibration
sensor(s) 1142, steering sensor(s) 1140, brake sensor(s) (e.g., as
part of the brake sensor system 1146), and/or other sensor
types.
[0101] One or more of the controller(s) 1136 may receive inputs
(e.g., represented by input data) from an instrument cluster 1132
of the vehicle 1100 and provide outputs (e.g., represented by
output data, display data, etc.) via a human-machine interface
(HMI) display 1134, an audible annunciator, a loudspeaker, and/or
via other components of the vehicle 1100. The outputs may include
information such as vehicle velocity, speed, time, map data (e.g.,
the HD map 1122 of FIG. 11C), location data (e.g., the vehicle's
1100 location, such as on a map), direction, location of other
vehicles (e.g., an occupancy grid), information about objects and
status of objects as perceived by the controller(s) 1136, etc. For
example, the HMI display 1134 may display information about the
presence of one or more objects (e.g., a street sign, caution sign,
traffic light changing, etc.), and/or information about driving
maneuvers the vehicle has made, is making, or will make (e.g.,
changing lanes now, taking exit 34B in two miles, etc.).
[0102] The vehicle 1100 further includes a network interface 1124
which may use one or more wireless antenna(s) 1126 and/or modem(s)
to communicate over one or more networks. For example, the network
interface 1124 may be capable of communication over LTE, WCDMA,
UMTS, GSM, CDMA2000, etc. The wireless antenna(s) 1126 may also
enable communication between objects in the environment (e.g.,
vehicles, mobile devices, etc.), using local area network(s), such
as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc., and/or low power
wide-area network(s) (LPWANs), such as LoRaWAN, SigFox, etc.
[0103] FIG. 11B is an example of camera locations and fields of
view for the example autonomous vehicle 1100 of FIG. 11A, in
accordance with some embodiments of the present disclosure. The
cameras and respective fields of view are one example embodiment
and are not intended to be limiting. For example, additional and/or
alternative cameras may be included and/or the cameras may be
located at different locations on the vehicle 1100.
[0104] The camera types for the cameras may include, but are not
limited to, digital cameras that may be adapted for use with the
components and/or systems of the vehicle 1100. The camera(s) may
operate at automotive safety integrity level (ASIL) B and/or at
another ASIL. The camera types may be capable of any image capture
rate, such as 60 frames per second (fps), 1120 fps, 240 fps, etc.,
depending on the embodiment. The cameras may be capable of using
rolling shutters, global shutters, another type of shutter, or a
combination thereof. In some examples, the color filter array may
include a red clear clear clear (RCCC) color filter array, a red
clear clear blue (RCCB) color filter array, a red blue green clear
(RBGC) color filter array, a Foveon X3 color filter array, a Bayer
sensors (RGGB) color filter array, a monochrome sensor color filter
array, and/or another type of color filter array. In some
embodiments, clear pixel cameras, such as cameras with an RCCC, an
RCCB, and/or an RBGC color filter array, may be used in an effort
to increase light sensitivity.
[0105] In some examples, one or more of the camera(s) may be used
to perform advanced driver assistance systems (ADAS) functions
(e.g., as part of a redundant or fail-safe design). For example, a
Multi-Function Mono Camera may be installed to provide functions
including lane departure warning, traffic sign assist and
intelligent headlamp control. One or more of the camera(s) (e.g.,
all of the cameras) may record and provide image data (e.g., video)
simultaneously.
[0106] One or more of the cameras may be mounted in a mounting
assembly, such as a custom designed (3-D printed) assembly, in
order to cut out stray light and reflections from within the car
(e.g., reflections from the dashboard reflected in the windshield
mirrors) which may interfere with the camera's image data capture
abilities. With reference to wing-mirror mounting assemblies, the
wing-mirror assemblies may be custom 3-D printed so that the camera
mounting plate matches the shape of the wing-mirror. In some
examples, the camera(s) may be integrated into the wing-mirror. For
side-view cameras, the camera(s) may also be integrated within the
four pillars at each corner of the cabin.
[0107] Cameras with a field of view that include portions of the
environment in front of the vehicle 1100 (e.g., front-facing
cameras) may be used for surround view, to help identify forward
facing paths and obstacles, as well aid in, with the help of one or
more controllers 1136 and/or control SoCs, providing information
critical to generating an occupancy grid and/or determining the
preferred vehicle paths. Front-facing cameras may be used to
perform many of the same ADAS functions as LIDAR, including
emergency braking, pedestrian detection, and collision avoidance.
Front-facing cameras may also be used for ADAS functions and
systems including Lane Departure Warnings ("LDW"), Autonomous
Cruise Control ("ACC"), and/or other functions such as traffic sign
recognition.
[0108] A variety of cameras may be used in a front-facing
configuration, including, for example, a monocular camera platform
that includes a CMOS (complementary metal oxide semiconductor)
color imager. Another example may be a wide-view camera(s) 1170
that may be used to perceive objects coming into view from the
periphery (e.g., pedestrians, crossing traffic or bicycles).
Although only one wide-view camera is illustrated in FIG. 11B,
there may any number of wide-view cameras 1170 on the vehicle 1100.
In addition, long-range camera(s) 1198 (e.g., a long-view stereo
camera pair) may be used for depth-based object detection,
especially for objects for which a neural network has not yet been
trained. The long-range camera(s) 1198 may also be used for object
detection and classification, as well as basic object tracking.
[0109] One or more stereo cameras 1168 may also be included in a
front-facing configuration. The stereo camera(s) 1168 may include
an integrated control unit comprising a scalable processing unit,
which may provide a programmable logic (FPGA) and a multi-core
micro-processor with an integrated CAN or Ethernet interface on a
single chip. Such a unit may be used to generate a 3-D map of the
vehicle's environment, including a distance estimate for all the
points in the image. An alternative stereo camera(s) 1168 may
include a compact stereo vision sensor(s) that may include two
camera lenses (one each on the left and right) and an image
processing chip that may measure the distance from the vehicle to
the target object and use the generated information (e.g.,
metadata) to activate the autonomous emergency braking and lane
departure warning functions. Other types of stereo camera(s) 1168
may be used in addition to, or alternatively from, those described
herein.
[0110] Cameras with a field of view that include portions of the
environment to the side of the vehicle 1100 (e.g., side-view
cameras) may be used for surround view, providing information used
to create and update the occupancy grid, as well as to generate
side impact collision warnings. For example, surround camera(s)
1174 (e.g., four surround cameras 1174 as illustrated in FIG. 11B)
may be positioned to on the vehicle 1100. The surround camera(s)
1174 may include wide-view camera(s) 1170, fisheye camera(s), 360
degree camera(s), and/or the like. Four example, four fisheye
cameras may be positioned on the vehicle's front, rear, and sides.
In an alternative arrangement, the vehicle may use three surround
camera(s) 1174 (e.g., left, right, and rear), and may leverage one
or more other camera(s) (e.g., a forward-facing camera) as a fourth
surround view camera.
[0111] Cameras with a field of view that include portions of the
environment to the rear of the vehicle 1100 (e.g., rear-view
cameras) may be used for park assistance, surround view, rear
collision warnings, and creating and updating the occupancy grid. A
wide variety of cameras may be used including, but not limited to,
cameras that are also suitable as a front-facing camera(s) (e.g.,
long-range and/or mid-range camera(s) 1198, stereo camera(s) 1168),
infrared camera(s) 1172, etc.), as described herein.
[0112] FIG. 11C is a block diagram of an example system
architecture for the example autonomous vehicle 1100 of FIG. 11A,
in accordance with some embodiments of the present disclosure. It
should be understood that this and other arrangements described
herein are set forth only as examples. Other arrangements and
elements (e.g., machines, interfaces, functions, orders, groupings
of functions, etc.) may be used in addition to or instead of those
shown, and some elements may be omitted altogether. Further, many
of the elements described herein are functional entities that may
be implemented as discrete or distributed components or in
conjunction with other components, and in any suitable combination
and location. Various functions described herein as being performed
by entities may be carried out by hardware, firmware, and/or
software. For instance, various functions may be carried out by a
processor executing instructions stored in memory.
[0113] Each of the components, features, and systems of the vehicle
1100 in FIG. 11C are illustrated as being connected via bus 1102.
The bus 1102 may include a Controller Area Network (CAN) data
interface (alternatively referred to herein as a "CAN bus"). A CAN
may be a network inside the vehicle 1100 used to aid in control of
various features and functionality of the vehicle 1100, such as
actuation of brakes, acceleration, braking, steering, windshield
wipers, etc. A CAN bus may be configured to have dozens or even
hundreds of nodes, each with its own unique identifier (e.g., a CAN
ID). The CAN bus may be read to find steering wheel angle, ground
speed, engine revolutions per minute (RPMs), button positions,
and/or other vehicle status indicators. The CAN bus may be ASIL B
compliant.
[0114] Although the bus 1102 is described herein as being a CAN
bus, this is not intended to be limiting. For example, in addition
to, or alternatively from, the CAN bus, FlexRay and/or Ethernet may
be used. Additionally, although a single line is used to represent
the bus 1102, this is not intended to be limiting. For example,
there may be any number of busses 1102, which may include one or
more CAN busses, one or more FlexRay busses, one or more Ethernet
busses, and/or one or more other types of busses using a different
protocol. In some examples, two or more busses 1102 may be used to
perform different functions, and/or may be used for redundancy. For
example, a first bus 1102 may be used for collision avoidance
functionality and a second bus 1102 may be used for actuation
control. In any example, each bus 1102 may communicate with any of
the components of the vehicle 1100, and two or more busses 1102 may
communicate with the same components. In some examples, each SoC
1104, each controller 1136, and/or each computer within the vehicle
may have access to the same input data (e.g., inputs from sensors
of the vehicle 1100), and may be connected to a common bus, such
the CAN bus.
[0115] The vehicle 1100 may include one or more controller(s) 1136,
such as those described herein with respect to FIG. 11A. The
controller(s) 1136 may be used for a variety of functions. The
controller(s) 1136 may be coupled to any of the various other
components and systems of the vehicle 1100, and may be used for
control of the vehicle 1100, artificial intelligence of the vehicle
1100, infotainment for the vehicle 1100, and/or the like.
[0116] The vehicle 1100 may include a system(s) on a chip (SoC)
1104. The SoC 1104 may include CPU(s) 1106, GPU(s) 1108,
processor(s) 1110, cache(s) 1112, accelerator(s) 1114, data
store(s) 1116, and/or other components and features not
illustrated. The SoC(s) 1104 may be used to control the vehicle
1100 in a variety of platforms and systems. For example, the SoC(s)
1104 may be combined in a system (e.g., the system of the vehicle
1100) with an HD map 1122 which may obtain map refreshes and/or
updates via a network interface 1124 from one or more servers
(e.g., server(s) 1178 of FIG. 11D).
[0117] The CPU(s) 1106 may include a CPU cluster or CPU complex
(alternatively referred to herein as a "CCPLEX"). The CPU(s) 1106
may include multiple cores and/or L2 caches. For example, in some
embodiments, the CPU(s) 1106 may include eight cores in a coherent
multi-processor configuration. In some embodiments, the CPU(s) 1106
may include four dual-core clusters where each cluster has a
dedicated L2 cache (e.g., a 2 MB L2 cache). The CPU(s) 1106 (e.g.,
the CCPLEX) may be configured to support simultaneous cluster
operation enabling any combination of the clusters of the CPU(s)
1106 to be active at any given time.
[0118] The CPU(s) 1106 may implement power management capabilities
that include one or more of the following features: individual
hardware blocks may be clock-gated automatically when idle to save
dynamic power; each core clock may be gated when the core is not
actively executing instructions due to execution of WFI/WFE
instructions; each core may be independently power-gated; each core
cluster may be independently clock-gated when all cores are
clock-gated or power-gated; and/or each core cluster can be
independently power-gated when all cores are power-gated. The
CPU(s) 1106 may further implement an enhanced algorithm for
managing power states, where allowed power states and expected
wakeup times are specified, and the hardware/microcode determines
the best power state to enter for the core, cluster, and CCPLEX.
The processing cores may support simplified power state entry
sequences in software with the work offloaded to microcode.
[0119] The GPU(s) 1108 may include an integrated GPU (alternatively
referred to herein as an "iGPU"). The GPU(s) 1108 may be
programmable and may be efficient for parallel workloads. The
GPU(s) 1108, in some examples, may use an enhanced tensor
instruction set. The GPU(s) 1108 may include one or more streaming
microprocessors, where each streaming microprocessor may include an
L1 cache (e.g., an L1 cache with at least 96 KB storage capacity),
and two or more of the streaming microprocessors may share an L2
cache (e.g., an L2 cache with a 512 KB storage capacity). In some
embodiments, the GPU(s) 1108 may include at least eight streaming
microprocessors. The GPU(s) 1108 may use compute application
programming interface(s) (API(s)). In addition, the GPU(s) 1108 may
use one or more parallel computing platforms and/or programming
models (e.g., NVIDIA's CUDA).
[0120] The GPU(s) 1108 may be power-optimized for best performance
in automotive and embedded use cases. For example, the GPU(s) 1108
may be fabricated on a Fin field-effect transistor (FinFET).
However, this is not intended to be limiting and the GPU(s) 1108
may be fabricated using other semiconductor manufacturing
processes. Each streaming microprocessor may incorporate a number
of mixed-precision processing cores partitioned into multiple
blocks. For example, and without limitation, 64 PF32 cores and 32
PF64 cores may be partitioned into four processing blocks. In such
an example, each processing block may be allocated 16 FP32 cores, 8
FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs
for deep learning matrix arithmetic, an L0 instruction cache, a
warp scheduler, a dispatch unit, and/or a 64 KB register file. In
addition, the streaming microprocessors may include independent
parallel integer and floating-point data paths to provide for
efficient execution of workloads with a mix of computation and
addressing calculations. The streaming microprocessors may include
independent thread scheduling capability to enable finer-grain
synchronization and cooperation between parallel threads. The
streaming microprocessors may include a combined L1 data cache and
shared memory unit in order to improve performance while
simplifying programming.
[0121] The GPU(s) 1108 may include a high bandwidth memory (HBM)
and/or a 16 GB HBM2 memory subsystem to provide, in some examples,
about 900 GB/second peak memory bandwidth. In some examples, in
addition to, or alternatively from, the HBM memory, a synchronous
graphics random-access memory (SGRAM) may be used, such as a
graphics double data rate type five synchronous random-access
memory (GDDR5).
[0122] The GPU(s) 1108 may include unified memory technology
including access counters to allow for more accurate migration of
memory pages to the processor that accesses them most frequently,
thereby improving efficiency for memory ranges shared between
processors. In some examples, address translation services (ATS)
support may be used to allow the GPU(s) 1108 to access the CPU(s)
1106 page tables directly. In such examples, when the GPU(s) 1108
memory management unit (MMU) experiences a miss, an address
translation request may be transmitted to the CPU(s) 1106. In
response, the CPU(s) 1106 may look in its page tables for the
virtual-to-physical mapping for the address and transmits the
translation back to the GPU(s) 1108. As such, unified memory
technology may allow a single unified virtual address space for
memory of both the CPU(s) 1106 and the GPU(s) 1108, thereby
simplifying the GPU(s) 1108 programming and porting of applications
to the GPU(s) 1108.
[0123] In addition, the GPU(s) 1108 may include an access counter
that may keep track of the frequency of access of the GPU(s) 1108
to memory of other processors. The access counter may help ensure
that memory pages are moved to the physical memory of the processor
that is accessing the pages most frequently.
[0124] The SoC(s) 1104 may include any number of cache(s) 1112,
including those described herein. For example, the cache(s) 1112
may include an L3 cache that is available to both the CPU(s) 1106
and the GPU(s) 1108 (e.g., that is connected both the CPU(s) 1106
and the GPU(s) 1108). The cache(s) 1112 may include a write-back
cache that may keep track of states of lines, such as by using a
cache coherence protocol (e.g., MEI, MESI, MSI, etc.). The L3 cache
may include 4 MB or more, depending on the embodiment, although
smaller cache sizes may be used.
[0125] The SoC(s) 1104 may include one or more accelerators 1114
(e.g., hardware accelerators, software accelerators, or a
combination thereof). For example, the SoC(s) 1104 may include a
hardware acceleration cluster that may include optimized hardware
accelerators and/or large on-chip memory. The large on-chip memory
(e.g., 4 MB of SRAM), may enable the hardware acceleration cluster
to accelerate neural networks and other calculations. The hardware
acceleration cluster may be used to complement the GPU(s) 1108 and
to off-load some of the tasks of the GPU(s) 1108 (e.g., to free up
more cycles of the GPU(s) 1108 for performing other tasks). As an
example, the accelerator(s) 1114 may be used for targeted workloads
(e.g., perception, convolutional neural networks (CNNs), etc.) that
are stable enough to be amenable to acceleration. The term "CNN,"
as used herein, may include all types of CNNs, including
region-based or regional convolutional neural networks (RCNNs) and
Fast RCNNs (e.g., as used for object detection).
[0126] The accelerator(s) 1114 (e.g., the hardware acceleration
cluster) may include a deep learning accelerator(s) (DLA). The
DLA(s) may include one or more Tensor processing units (TPUs) that
may be configured to provide an additional ten trillion operations
per second for deep learning applications and inferencing. The TPUs
may be accelerators configured to, and optimized for, performing
image processing functions (e.g., for CNNs, RCNNs, etc.). The
DLA(s) may further be optimized for a specific set of neural
network types and floating point operations, as well as
inferencing. The design of the DLA(s) may provide more performance
per millimeter than a general-purpose GPU, and vastly exceeds the
performance of a CPU. The TPU(s) may perform several functions,
including a single-instance convolution function, supporting, for
example, INT8, INT16, and FP16 data types for both features and
weights, as well as post-processor functions.
[0127] The DLA(s) may quickly and efficiently execute neural
networks, especially CNNs, on processed or unprocessed data for any
of a variety of functions, including, for example and without
limitation: a CNN for object identification and detection using
data from camera sensors; a CNN for distance estimation using data
from camera sensors; a CNN for emergency vehicle detection and
identification and detection using data from microphones; a CNN for
facial recognition and vehicle owner identification using data from
camera sensors; and/or a CNN for security and/or safety related
events.
[0128] The DLA(s) may perform any function of the GPU(s) 1108, and
by using an inference accelerator, for example, a designer may
target either the DLA(s) or the GPU(s) 1108 for any function. For
example, the designer may focus processing of CNNs and floating
point operations on the DLA(s) and leave other functions to the
GPU(s) 1108 and/or other accelerator(s) 1114.
[0129] The accelerator(s) 1114 (e.g., the hardware acceleration
cluster) may include a programmable vision accelerator(s) (PVA),
which may alternatively be referred to herein as a computer vision
accelerator. The PVA(s) may be designed and configured to
accelerate computer vision algorithms for the advanced driver
assistance systems (ADAS), autonomous driving, and/or augmented
reality (AR) and/or virtual reality (VR) applications. The PVA(s)
may provide a balance between performance and flexibility. For
example, each PVA(s) may include, for example and without
limitation, any number of reduced instruction set computer (RISC)
cores, direct memory access (DMA), and/or any number of vector
processors.
[0130] The RISC cores may interact with image sensors (e.g., the
image sensors of any of the cameras described herein), image signal
processor(s), and/or the like. Each of the RISC cores may include
any amount of memory. The RISC cores may use any of a number of
protocols, depending on the embodiment. In some examples, the RISC
cores may execute a real-time operating system (RTOS). The RISC
cores may be implemented using one or more integrated circuit
devices, application specific integrated circuits (ASICs), and/or
memory devices. For example, the RISC cores may include an
instruction cache and/or a tightly coupled RAM.
[0131] The DMA may enable components of the PVA(s) to access the
system memory independently of the CPU(s) 1106. The DMA may support
any number of features used to provide optimization to the PVA
including, but not limited to, supporting multi-dimensional
addressing and/or circular addressing. In some examples, the DMA
may support up to six or more dimensions of addressing, which may
include block width, block height, block depth, horizontal block
stepping, vertical block stepping, and/or depth stepping.
[0132] The vector processors may be programmable processors that
may be designed to efficiently and flexibly execute programming for
computer vision algorithms and provide signal processing
capabilities. In some examples, the PVA may include a PVA core and
two vector processing subsystem partitions. The PVA core may
include a processor subsystem, DMA engine(s) (e.g., two DMA
engines), and/or other peripherals. The vector processing subsystem
may operate as the primary processing engine of the PVA, and may
include a vector processing unit (VPU), an instruction cache,
and/or vector memory (e.g., VMEM). A VPU core may include a digital
signal processor such as, for example, a single instruction,
multiple data (SIMD), very long instruction word (VLIW) digital
signal processor. The combination of the SIMD and VLIW may enhance
throughput and speed.
[0133] Each of the vector processors may include an instruction
cache and may be coupled to dedicated memory. As a result, in some
examples, each of the vector processors may be configured to
execute independently of the other vector processors. In other
examples, the vector processors that are included in a particular
PVA may be configured to employ data parallelism. For example, in
some embodiments, the plurality of vector processors included in a
single PVA may execute the same computer vision algorithm, but on
different regions of an image. In other examples, the vector
processors included in a particular PVA may simultaneously execute
different computer vision algorithms, on the same image, or even
execute different algorithms on sequential images or portions of an
image. Among other things, any number of PVAs may be included in
the hardware acceleration cluster and any number of vector
processors may be included in each of the PVAs. In addition, the
PVA(s) may include additional error correcting code (ECC) memory,
to enhance overall system safety.
[0134] The accelerator(s) 1114 (e.g., the hardware acceleration
cluster) may include a computer vision network on-chip and SRAM,
for providing a high-bandwidth, low latency SRAM for the
accelerator(s) 1114. In some examples, the on-chip memory may
include at least 4 MB SRAM, consisting of, for example and without
limitation, eight field-configurable memory blocks, that may be
accessible by both the PVA and the DLA. Each pair of memory blocks
may include an advanced peripheral bus (APB) interface,
configuration circuitry, a controller, and a multiplexer. Any type
of memory may be used. The PVA and DLA may access the memory via a
backbone that provides the PVA and DLA with high-speed access to
memory. The backbone may include a computer vision network on-chip
that interconnects the PVA and the DLA to the memory (e.g., using
the APB).
[0135] The computer vision network on-chip may include an interface
that determines, before transmission of any control
signal/address/data, that both the PVA and the DLA provide ready
and valid signals. Such an interface may provide for separate
phases and separate channels for transmitting control
signals/addresses/data, as well as burst-type communications for
continuous data transfer. This type of interface may comply with
ISO 26262 or IEC 612508 standards, although other standards and
protocols may be used.
[0136] In some examples, the SoC(s) 1104 may include a real-time
ray-tracing hardware accelerator, such as described in U.S. patent
application Ser. No. 16/101,1232, filed on Aug. 10, 2018. The
real-time ray-tracing hardware accelerator may be used to quickly
and efficiently determine the positions and extents of objects
(e.g., within a world model), to generate real0time visualization
simulations, for RADAR signal interpretation, for sound propagation
synthesis and/or analysis, for simulation of SONAR systems, for
general wave propagation simulation, for comparison to LIDAR data
for purposes of localization and/or other functions, and/or for
other uses.
[0137] The accelerator(s) 1114 (e.g., the hardware accelerator
cluster) have a wide array of uses for autonomous driving. The PVA
may be a programmable vision accelerator that may be used for key
processing stages in ADAS and autonomous vehicles. The PVA's
capabilities are a good match for algorithmic domains needing
predictable processing, at low power and low latency. In other
words, the PVA performs well on semi-dense or dense regular
computation, even on small data sets, which need predictable
run-times with low latency and low power. Thus, in the context of
platforms for autonomous vehicles, the PVAs are designed to run
classic computer vision algorithms, as they are efficient at object
detection and operating on integer math.
[0138] For example, according to one embodiment of the technology,
the PVA is used to perform computer stereo vision. A semi-global
matching-based algorithm may be used in some examples, although
this is not intended to be limiting. Many applications for Level
3-5 autonomous driving require motion estimation/stereo matching
on-the-fly (e.g., structure from motion, pedestrian recognition,
lane detection, etc.). The PVA may perform computer stereo vision
function on inputs from two monocular cameras.
[0139] In some examples, the PVA may be used to perform dense
optical flow. According to process raw RADAR data (e.g., using a 4D
Fast Fourier Transform) to provide Processed RADAR. In other
examples, the PVA is used for time of flight depth processing, by
processing raw time of flight data to provide processed time of
flight data, for example.
[0140] The DLA may be used to run any type of network to enhance
control and driving safety, including for example, a neural network
that outputs a measure of confidence for each object detection.
Such a confidence value may be interpreted as a probability, or as
providing a relative "weight" of each detection compared to other
detections. This confidence value enables the system to make
further decisions regarding which detections should be considered
as true positive detections rather than false positive detections.
For example, the system may set a threshold value for the
confidence and consider only the detections exceeding the threshold
value as true positive detections. In an automatic emergency
braking (AEB) system, false positive detections would cause the
vehicle to automatically perform emergency braking, which is
obviously undesirable. Therefore, only the most confident
detections should be considered as triggers for AEB. The DLA may
run a neural network for regressing the confidence value. The
neural network may take as its input at least some subset of
parameters, such as bounding box dimensions, ground plane estimate
obtained (e.g. from another subsystem), inertial measurement unit
(IMU) sensor 1166 output that correlates with the vehicle 1100
orientation, distance, 3D location estimates of the object obtained
from the neural network and/or other sensors (e.g., LIDAR sensor(s)
1164 or RADAR sensor(s) 1160), among others.
[0141] The SoC(s) 1104 may include data store(s) 1116 (e.g.,
memory). The data store(s) 1116 may be on-chip memory of the SoC(s)
1104, which may store neural networks to be executed on the GPU
and/or the DLA. In some examples, the data store(s) 1116 may be
large enough in capacity to store multiple instances of neural
networks for redundancy and safety. The data store(s) 1112 may
comprise L2 or L3 cache(s) 1112. Reference to the data store(s)
1116 may include reference to the memory associated with the PVA,
DLA, and/or other accelerator(s) 1114, as described herein.
[0142] The SoC(s) 1104 may include one or more processor(s) 1110
(e.g., embedded processors). The processor(s) 1110 may include a
boot and power management processor that may be a dedicated
processor and subsystem to handle boot power and management
functions and related security enforcement. The boot and power
management processor may be a part of the SoC(s) 1104 boot sequence
and may provide runtime power management services. The boot power
and management processor may provide clock and voltage programming,
assistance in system low power state transitions, management of
SoC(s) 1104 thermals and temperature sensors, and/or management of
the SoC(s) 1104 power states. Each temperature sensor may be
implemented as a ring-oscillator whose output frequency is
proportional to temperature, and the SoC(s) 1104 may use the
ring-oscillators to detect temperatures of the CPU(s) 1106, GPU(s)
1108, and/or accelerator(s) 1114. If temperatures are determined to
exceed a threshold, the boot and power management processor may
enter a temperature fault routine and put the SoC(s) 1104 into a
lower power state and/or put the vehicle 1100 into a chauffeur to
safe stop mode (e.g., bring the vehicle 1100 to a safe stop).
[0143] The processor(s) 1110 may further include a set of embedded
processors that may serve as an audio processing engine. The audio
processing engine may be an audio subsystem that enables full
hardware support for multi-channel audio over multiple interfaces,
and a broad and flexible range of audio 110 interfaces. In some
examples, the audio processing engine is a dedicated processor core
with a digital signal processor with dedicated RAM.
[0144] The processor(s) 1110 may further include an always on
processor engine that may provide necessary hardware features to
support low power sensor management and wake use cases. The always
on processor engine may include a processor core, a tightly coupled
RAM, supporting peripherals (e.g., timers and interrupt
controllers), various 110 controller peripherals, and routing
logic.
[0145] The processor(s) 1110 may further include a safety cluster
engine that includes a dedicated processor subsystem to handle
safety management for automotive applications. The safety cluster
engine may include two or more processor cores, a tightly coupled
RAM, support peripherals (e.g., timers, an interrupt controller,
etc.), and/or routing logic. In a safety mode, the two or more
cores may operate in a lockstep mode and function as a single core
with comparison logic to detect any differences between their
operations.
[0146] The processor(s) 1110 may further include a real-time camera
engine that may include a dedicated processor subsystem for
handling real-time camera management.
[0147] The processor(s) 1110 may further include a high-dynamic
range signal processor that may include an image signal processor
that is a hardware engine that is part of the camera processing
pipeline.
[0148] The processor(s) 1110 may include a video image compositor
that may be a processing block (e.g., implemented on a
microprocessor) that implements video post-processing functions
needed by a video playback application to produce the final image
for the player window. The video image compositor may perform lens
distortion correction on wide-view camera(s) 1170, surround
camera(s) 1174, and/or on in-cabin monitoring camera sensors.
In-cabin monitoring camera sensor is preferably monitored by a
neural network running on another instance of the Advanced SoC,
configured to identify in cabin events and respond accordingly. An
in-cabin system may perform lip reading to activate cellular
service and place a phone call, dictate emails, change the
vehicle's destination, activate or change the vehicle's
infotainment system and settings, or provide voice-activated web
surfing. Certain functions are available to the driver only when
the vehicle is operating in an autonomous mode, and are disabled
otherwise.
[0149] The video image compositor may include enhanced temporal
noise reduction for both spatial and temporal noise reduction. For
example, where motion occurs in a video, the noise reduction
weights spatial information appropriately, decreasing the weight of
information provided by adjacent frames. Where an image or portion
of an image does not include motion, the temporal noise reduction
performed by the video image compositor may use information from
the previous image to reduce noise in the current image.
[0150] The video image compositor may also be configured to perform
stereo rectification on input stereo lens frames. The video image
compositor may further be used for user interface composition when
the operating system desktop is in use, and the GPU(s) 1108 is not
required to continuously render new surfaces. Even when the GPU(s)
1108 is powered on and active doing 3D rendering, the video image
compositor may be used to offload the GPU(s) 1108 to improve
performance and responsiveness.
[0151] The SoC(s) 1104 may further include a mobile industry
processor interface (MIPI) camera serial interface for receiving
video and input from cameras, a high-speed interface, and/or a
video input block that may be used for camera and related pixel
input functions. The SoC(s) 1104 may further include an
input/output controller(s) that may be controlled by software and
may be used for receiving I/O signals that are uncommitted to a
specific role.
[0152] The SoC(s) 1104 may further include a broad range of
peripheral interfaces to enable communication with peripherals,
audio codecs, power management, and/or other devices. The SoC(s)
1104 may be used to process data from cameras (e.g., connected over
Gigabit Multimedia Serial Link and Ethernet), sensors (e.g., LIDAR
sensor(s) 1164, RADAR sensor(s) 1160, etc. that may be connected
over Ethernet), data from bus 1102 (e.g., speed of vehicle 1100,
steering wheel position, etc.), data from GNSS sensor(s) 1158
(e.g., connected over Ethernet or CAN bus). The SoC(s) 1104 may
further include dedicated high-performance mass storage controllers
that may include their own DMA engines, and that may be used to
free the CPU(s) 1106 from routine data management tasks.
[0153] The SoC(s) 1104 may be an end-to-end platform with a
flexible architecture that spans automation levels 3-5, thereby
providing a comprehensive functional safety architecture that
leverages and makes efficient use of computer vision and ADAS
techniques for diversity and redundancy, provides a platform for a
flexible, reliable driving software stack, along with deep learning
tools. The SoC(s) 1104 may be faster, more reliable, and even more
energy-efficient and space-efficient than conventional systems. For
example, the accelerator(s) 1114, when combined with the CPU(s)
1106, the GPU(s) 1108, and the data store(s) 1116, may provide for
a fast, efficient platform for level 3-5 autonomous vehicles.
[0154] The technology thus provides capabilities and functionality
that cannot be achieved by conventional systems. For example,
computer vision algorithms may be executed on CPUs, which may be
configured using high-level programming language, such as the C
programming language, to execute a wide variety of processing
algorithms across a wide variety of visual data. However, CPUs are
oftentimes unable to meet the performance requirements of many
computer vision applications, such as those related to execution
time and power consumption, for example. In particular, many CPUs
are unable to execute complex object detection algorithms in
real-time, which is a requirement of in-vehicle ADAS applications,
and a requirement for practical Level 3-5 autonomous vehicles.
[0155] In contrast to conventional systems, by providing a CPU
complex, GPU complex, and a hardware acceleration cluster, the
technology described herein allows for multiple neural networks to
be performed simultaneously and/or sequentially, and for the
results to be combined together to enable Level 3-5 autonomous
driving functionality. For example, a CNN executing on the DLA or
dGPU (e.g., the GPU(s) 1120) may include a text and word
recognition, allowing the supercomputer to read and understand
traffic signs, including signs for which the neural network has not
been specifically trained. The DLA may further include a neural
network that is able to identify, interpret, and provides semantic
understanding of the sign, and to pass that semantic understanding
to the path planning modules running on the CPU Complex.
[0156] As another example, multiple neural networks may be run
simultaneously, as is required for Level 3, 4, or 5 driving. For
example, a warning sign consisting of "Caution: flashing lights
indicate icy conditions," along with an electric light, may be
independently or collectively interpreted by several neural
networks. The sign itself may be identified as a traffic sign by a
first deployed neural network (e.g., a neural network that has been
trained), the text "Flashing lights indicate icy conditions" may be
interpreted by a second deployed neural network, which informs the
vehicle's path planning software (preferably executing on the CPU
Complex) that when flashing lights are detected, icy conditions
exist. The flashing light may be identified by operating a third
deployed neural network over multiple frames, informing the
vehicle's path-planning software of the presence (or absence) of
flashing lights. All three neural networks may run simultaneously,
such as within the DLA and/or on the GPU(s) 1108.
[0157] In some examples, a CNN for facial recognition and vehicle
owner identification may use data from camera sensors to identify
the presence of an authorized driver and/or owner of the vehicle
1100. The always on sensor processing engine may be used to unlock
the vehicle when the owner approaches the driver door and turn on
the lights, and, in security mode, to disable the vehicle when the
owner leaves the vehicle. In this way, the SoC(s) 1104 provide for
security against theft and/or carjacking.
[0158] In another example, a CNN for emergency vehicle detection
and identification may use data from microphones 1196 to detect and
identify emergency vehicle sirens. In contrast to conventional
systems, that use general classifiers to detect sirens and manually
extract features, the SoC(s) 1104 use the CNN for classifying
environmental and urban sounds, as well as classifying visual data.
In a preferred embodiment, the CNN running on the DLA is trained to
identify the relative closing speed of the emergency vehicle (e.g.,
by using the Doppler effect). The CNN may also be trained to
identify emergency vehicles specific to the local area in which the
vehicle is operating, as identified by GNSS sensor(s) 1158. Thus,
for example, when operating in Europe the CNN will seek to detect
European sirens, and when in the United States the CNN will seek to
identify only North American sirens. Once an emergency vehicle is
detected, a control program may be used to execute an emergency
vehicle safety routine, slowing the vehicle, pulling over to the
side of the road, parking the vehicle, and/or idling the vehicle,
with the assistance of ultrasonic sensors 1162, until the emergency
vehicle(s) passes.
[0159] The vehicle may include a CPU(s) 1118 (e.g., discrete
CPU(s), or dCPU(s)), that may be coupled to the SoC(s) 1104 via a
high-speed interconnect (e.g., PCIe). The CPU(s) 1118 may include
an X86 processor, for example. The CPU(s) 1118 may be used to
perform any of a variety of functions, including arbitrating
potentially inconsistent results between ADAS sensors and the
SoC(s) 1104, and/or monitoring the status and health of the
controller(s) 1136 and/or infotainment SoC 1130, for example.
[0160] The vehicle 1100 may include a GPU(s) 1120 (e.g., discrete
GPU(s), or dGPU(s)), that may be coupled to the SoC(s) 1104 via a
high-speed interconnect (e.g., NVIDIA's NVLINK). The GPU(s) 1120
may provide additional artificial intelligence functionality, such
as by executing redundant and/or different neural networks, and may
be used to train and/or update neural networks based at least in
part on input (e.g., sensor data) from sensors of the vehicle
1100.
[0161] The vehicle 1100 may further include the network interface
1124 which may include one or more wireless antennas 1126 (e.g.,
one or more wireless antennas for different communication
protocols, such as a cellular antenna, a Bluetooth antenna, etc.).
The network interface 1124 may be used to enable wireless
connectivity over the Internet with the cloud (e.g., with the
server(s) 1178 and/or other network devices), with other vehicles,
and/or with computing devices (e.g., client devices of passengers).
To communicate with other vehicles, a direct link may be
established between the two vehicles and/or an indirect link may be
established (e.g., across networks and over the Internet). Direct
links may be provided using a vehicle-to-vehicle communication
link. The vehicle-to-vehicle communication link may provide the
vehicle 1100 information about vehicles in proximity to the vehicle
1100 (e.g., vehicles in front of, on the side of, and/or behind the
vehicle 1100). This functionality may be part of a cooperative
adaptive cruise control functionality of the vehicle 1100.
[0162] The network interface 1124 may include a SoC that provides
modulation and demodulation functionality and enables the
controller(s) 1136 to communicate over wireless networks. The
network interface 1124 may include a radio frequency front-end for
up-conversion from baseband to radio frequency, and down conversion
from radio frequency to baseband. The frequency conversions may be
performed through well-known processes, and/or may be performed
using super-heterodyne processes. In some examples, the radio
frequency front end functionality may be provided by a separate
chip. The network interface may include wireless functionality for
communicating over LTE, WCDMA, UMTS, GSM, CDMA2000, Bluetooth,
Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or other wireless
protocols.
[0163] The vehicle 1100 may further include data store(s) 1128
which may include off-chip (e.g., off the SoC(s) 1104) storage. The
data store(s) 1128 may include one or more storage elements
including RAM, SRAM, DRAM, VRAM, Flash, hard disks, and/or other
components and/or devices that may store at least one bit of
data.
[0164] The vehicle 1100 may further include GNSS sensor(s) 1158.
The GNSS sensor(s) 1158 (e.g., GPS and/or assisted GPS sensors), to
assist in mapping, perception, occupancy grid generation, and/or
path planning functions. Any number of GNSS sensor(s) 1158 may be
used, including, for example and without limitation, a GPS using a
USB connector with an Ethernet to Serial (RS-232) bridge.
[0165] The vehicle 1100 may further include RADAR sensor(s) 1160.
The RADAR sensor(s) 1160 may be used by the vehicle 1100 for
long-range vehicle detection, even in darkness and/or severe
weather conditions. RADAR functional safety levels may be ASIL B.
The RADAR sensor(s) 1160 may use the CAN and/or the bus 1102 (e.g.,
to transmit data generated by the RADAR sensor(s) 1160) for control
and to access object tracking data, with access to Ethernet to
access raw data in some examples. A wide variety of RADAR sensor
types may be used. For example, and without limitation, the RADAR
sensor(s) 1160 may be suitable for front, rear, and side RADAR use.
In some example, Pulse Doppler RADAR sensor(s) are used.
[0166] The RADAR sensor(s) 1160 may include different
configurations, such as long range with narrow field of view, short
range with wide field of view, short range side coverage, etc. In
some examples, long-range RADAR may be used for adaptive cruise
control functionality. The long-range RADAR systems may provide a
broad field of view realized by two or more independent scans, such
as within a 250 m range. The RADAR sensor(s) 1160 may help in
distinguishing between static and moving objects, and may be used
by ADAS systems for emergency brake assist and forward collision
warning. Long-range RADAR sensors may include monostatic multimodal
RADAR with multiple (e.g., six or more) fixed RADAR antennae and a
high-speed CAN and FlexRay interface. In an example with six
antennae, the central four antennae may create a focused beam
pattern, designed to record the vehicle's 1100 surroundings at
higher speeds with minimal interference from traffic in adjacent
lanes. The other two antennae may expand the field of view, making
it possible to quickly detect vehicles entering or leaving the
vehicle's 1100 lane.
[0167] Mid-range RADAR systems may include, as an example, a range
of up to 1460 m (front) or 80 m (rear), and a field of view of up
to 42 degrees (front) or 1450 degrees (rear). Short-range RADAR
systems may include, without limitation, RADAR sensors designed to
be installed at both ends of the rear bumper. When installed at
both ends of the rear bumper, such a RADAR sensor systems may
create two beams that constantly monitor the blind spot in the rear
and next to the vehicle.
[0168] Short-range RADAR systems may be used in an ADAS system for
blind spot detection and/or lane change assist.
[0169] The vehicle 1100 may further include ultrasonic sensor(s)
1162. The ultrasonic sensor(s) 1162, which may be positioned at the
front, back, and/or the sides of the vehicle 1100, may be used for
park assist and/or to create and update an occupancy grid. A wide
variety of ultrasonic sensor(s) 1162 may be used, and different
ultrasonic sensor(s) 1162 may be used for different ranges of
detection (e.g., 2.5 m, 4 m). The ultrasonic sensor(s) 1162 may
operate at functional safety levels of ASIL B.
[0170] The vehicle 1100 may include LIDAR sensor(s) 1164. The LIDAR
sensor(s) 1164 may be used for object and pedestrian detection,
emergency braking, collision avoidance, and/or other functions. The
LIDAR sensor(s) 1164 may be functional safety level ASIL B. In some
examples, the vehicle 1100 may include multiple LIDAR sensors 1164
(e.g., two, four, six, etc.) that may use Ethernet (e.g., to
provide data to a Gigabit Ethernet switch).
[0171] In some examples, the LIDAR sensor(s) 1164 may be capable of
providing a list of objects and their distances for a 360-degree
field of view. Commercially available LIDAR sensor(s) 1164 may have
an advertised range of approximately 1400 m, with an accuracy of 2
cm-3 cm, and with support for a 1400 Mbps Ethernet connection, for
example. In some examples, one or more non-protruding LIDAR sensors
1164 may be used. In such examples, the LIDAR sensor(s) 1164 may be
implemented as a small device that may be embedded into the front,
rear, sides, and/or corners of the vehicle 1100. The LIDAR
sensor(s) 1164, in such examples, may provide up to a 1420-degree
horizontal and 35-degree vertical field-of-view, with a 200 m range
even for low-reflectivity objects. Front-mounted LIDAR sensor(s)
1164 may be configured for a horizontal field of view between 45
degrees and 135 degrees.
[0172] In some examples, LIDAR technologies, such as 3D flash
LIDAR, may also be used. 3D Flash LIDAR uses a flash of a laser as
a transmission source, to illuminate vehicle surroundings up to
approximately 200 m. A flash LIDAR unit includes a receptor, which
records the laser pulse transit time and the reflected light on
each pixel, which in turn corresponds to the range from the vehicle
to the objects. Flash LIDAR may allow for highly accurate and
distortion-free images of the surroundings to be generated with
every laser flash. In some examples, four flash LIDAR sensors may
be deployed, one at each side of the vehicle 1100. Available 3D
flash LIDAR systems include a solid-state 3D staring array LIDAR
camera with no moving parts other than a fan (e.g., a non-scanning
LIDAR device). The flash LIDAR device may use a 5 nanosecond class
I (eye-safe) laser pulse per frame and may capture the reflected
laser light in the form of 3D range point clouds and co-registered
intensity data. By using flash LIDAR, and because flash LIDAR is a
solid-state device with no moving parts, the LIDAR sensor(s) 1164
may be less susceptible to motion blur, vibration, and/or
shock.
[0173] The vehicle may further include IMU sensor(s) 1166. The IMU
sensor(s) 1166 may be located at a center of the rear axle of the
vehicle 1100, in some examples. The IMU sensor(s) 1166 may include,
for example and without limitation, an accelerometer(s), a
magnetometer(s), a gyroscope(s), a magnetic compass(es), and/or
other sensor types. In some examples, such as in six-axis
applications, the IMU sensor(s) 1166 may include accelerometers and
gyroscopes, while in nine-axis applications, the IMU sensor(s) 1166
may include accelerometers, gyroscopes, and magnetometers.
[0174] In some embodiments, the IMU sensor(s) 1166 may be
implemented as a miniature, high performance GPS-Aided Inertial
Navigation System (GPS/INS) that combines micro-electro-mechanical
systems (MEMS) inertial sensors, a high-sensitivity GPS receiver,
and advanced Kalman filtering algorithms to provide estimates of
position, velocity, and attitude. As such, in some examples, the
IMU sensor(s) 1166 may enable the vehicle 1100 to estimate heading
without requiring input from a magnetic sensor by directly
observing and correlating the changes in velocity from GPS to the
IMU sensor(s) 1166. In some examples, the IMU sensor(s) 1166 and
the GNSS sensor(s) 1158 may be combined in a single integrated
unit.
[0175] The vehicle may include microphone(s) 1196 placed in and/or
around the vehicle 1100. The microphone(s) 1196 may be used for
emergency vehicle detection and identification, among other
things.
[0176] The vehicle may further include any number of camera types,
including stereo camera(s) 1168, wide-view camera(s) 1170, infrared
camera(s) 1172, surround camera(s) 1174, long-range and/or
mid-range camera(s) 1198, and/or other camera types. The cameras
may be used to capture image data around an entire periphery of the
vehicle 1100. The types of cameras used depends on the embodiments
and requirements for the vehicle 1100, and any combination of
camera types may be used to provide the necessary coverage around
the vehicle 1100. In addition, the number of cameras may differ
depending on the embodiment. For example, the vehicle may include
six cameras, seven cameras, ten cameras, twelve cameras, and/or
another number of cameras. The cameras may support, as an example
and without limitation, Gigabit Multimedia Serial Link (GMSL)
and/or Gigabit Ethernet. Each of the camera(s) is described with
more detail herein with respect to FIG. 11A and FIG. 11B.
[0177] The vehicle 1100 may further include vibration sensor(s)
1142. The vibration sensor(s) 1142 may measure vibrations of
components of the vehicle, such as the axle(s). For example,
changes in vibrations may indicate a change in road surfaces. In
another example, when two or more vibration sensors 1142 are used,
the differences between the vibrations may be used to determine
friction or slippage of the road surface (e.g., when the difference
in vibration is between a power-driven axle and a freely rotating
axle).
[0178] The vehicle 1100 may include an ADAS system 1138. The ADAS
system 1138 may include a SoC, in some examples. The ADAS system
1138 may include autonomous/adaptive/automatic cruise control
(ACC), cooperative adaptive cruise control (CACC), forward crash
warning (FCW), automatic emergency braking (AEB), lane departure
warnings (LDW), lane keep assist (LKA), blind spot warning (BSW),
rear cross-traffic warning (RCTW), collision warning systems (CWS),
lane centering (LC), and/or other features and functionality.
[0179] The ACC systems may use RADAR sensor(s) 1160, LIDAR
sensor(s) 1164, and/or a camera(s). The ACC systems may include
longitudinal ACC and/or lateral ACC. Longitudinal ACC monitors and
controls the distance to the vehicle immediately ahead of the
vehicle 1100 and automatically adjust the vehicle speed to maintain
a safe distance from vehicles ahead. Lateral ACC performs distance
keeping, and advises the vehicle 1100 to change lanes when
necessary. Lateral ACC is related to other ADAS applications such
as LCA and CWS.
[0180] CACC uses information from other vehicles that may be
received via the network interface 1124 and/or the wireless
antenna(s) 1126 from other vehicles via a wireless link, or
indirectly, over a network connection (e.g., over the Internet).
Direct links may be provided by a vehicle-to-vehicle (V2V)
communication link, while indirect links may be
infrastructure-to-vehicle (I2V) communication link. In general, the
V2V communication concept provides information about the
immediately preceding vehicles (e.g., vehicles immediately ahead of
and in the same lane as the vehicle 1100), while the I2V
communication concept provides information about traffic further
ahead. CACC systems may include either or both I2V and V2V
information sources. Given the information of the vehicles ahead of
the vehicle 1100, CACC may be more reliable and it has potential to
improve traffic flow smoothness and reduce congestion on the
road.
[0181] FCW systems are designed to alert the driver to a hazard, so
that the driver may take corrective action. FCW systems use a
front-facing camera and/or RADAR sensor(s) 1160, coupled to a
dedicated processor, DSP, FPGA, and/or ASIC, that is electrically
coupled to driver feedback, such as a display, speaker, and/or
vibrating component. FCW systems may provide a warning, such as in
the form of a sound, visual warning, vibration and/or a quick brake
pulse.
[0182] AEB systems detect an impending forward collision with
another vehicle or other object, and may automatically apply the
brakes if the driver does not take corrective action within a
specified time or distance parameter. AEB systems may use
front-facing camera(s) and/or RADAR sensor(s) 1160, coupled to a
dedicated processor, DSP, FPGA, and/or ASIC. When the AEB system
detects a hazard, it typically first alerts the driver to take
corrective action to avoid the collision and, if the driver does
not take corrective action, the AEB system may automatically apply
the brakes in an effort to prevent, or at least mitigate, the
impact of the predicted collision. AEB systems, may include
techniques such as dynamic brake support and/or crash imminent
braking.
[0183] LDW systems provide visual, audible, and/or tactile
warnings, such as steering wheel or seat vibrations, to alert the
driver when the vehicle 1100 crosses lane markings. A LDW system
does not activate when the driver indicates an intentional lane
departure, by activating a turn signal. LDW systems may use
front-side facing cameras, coupled to a dedicated processor, DSP,
FPGA, and/or ASIC, that is electrically coupled to driver feedback,
such as a display, speaker, and/or vibrating component.
[0184] LKA systems are a variation of LDW systems. LKA systems
provide steering input or braking to correct the vehicle 1100 if
the vehicle 1100 starts to exit the lane.
[0185] BSW systems detects and warn the driver of vehicles in an
automobile's blind spot. BSW systems may provide a visual, audible,
and/or tactile alert to indicate that merging or changing lanes is
unsafe. The system may provide an additional warning when the
driver uses a turn signal. BSW systems may use rear-side facing
camera(s) and/or RADAR sensor(s) 1160, coupled to a dedicated
processor, DSP, FPGA, and/or ASIC, that is electrically coupled to
driver feedback, such as a display, speaker, and/or vibrating
component.
[0186] RCTW systems may provide visual, audible, and/or tactile
notification when an object is detected outside the rear-camera
range when the vehicle 1100 is backing up. Some RCTW systems
include AEB to ensure that the vehicle brakes are applied to avoid
a crash. RCTW systems may use one or more rear-facing RADAR
sensor(s) 1160, coupled to a dedicated processor, DSP, FPGA, and/or
ASIC, that is electrically coupled to driver feedback, such as a
display, speaker, and/or vibrating component.
[0187] Conventional ADAS systems may be prone to false positive
results which may be annoying and distracting to a driver, but
typically are not catastrophic, because the ADAS systems alert the
driver and allow the driver to decide whether a safety condition
truly exists and act accordingly. However, in an autonomous vehicle
1100, the vehicle 1100 itself must, in the case of conflicting
results, decide whether to heed the result from a primary computer
or a secondary computer (e.g., a first controller 1136 or a second
controller 1136). For example, in some embodiments, the ADAS system
1138 may be a backup and/or secondary computer for providing
perception information to a backup computer rationality module. The
backup computer rationality monitor may run a redundant diverse
software on hardware components to detect faults in perception and
dynamic driving tasks. Outputs from the ADAS system 1138 may be
provided to a supervisory MCU. If outputs from the primary computer
and the secondary computer conflict, the supervisory MCU must
determine how to reconcile the conflict to ensure safe
operation.
[0188] In some examples, the primary computer may be configured to
provide the supervisory MCU with a confidence score, indicating the
primary computer's confidence in the chosen result. If the
confidence score exceeds a threshold, the supervisory MCU may
follow the primary computer's direction, regardless of whether the
secondary computer provides a conflicting or inconsistent result.
Where the confidence score does not meet the threshold, and where
the primary and secondary computer indicate different results
(e.g., the conflict), the supervisory MCU may arbitrate between the
computers to determine the appropriate outcome.
[0189] The supervisory MCU may be configured to run a neural
network(s) that is trained and configured to determine, based at
least in part on outputs from the primary computer and the
secondary computer, conditions under which the secondary computer
provides false alarms. Thus, the neural network(s) in the
supervisory MCU may learn when the secondary computer's output may
be trusted, and when it cannot. For example, when the secondary
computer is a RADAR-based FCW system, a neural network(s) in the
supervisory MCU may learn when the FCW system is identifying
metallic objects that are not, in fact, hazards, such as a drainage
grate or manhole cover that triggers an alarm. Similarly, when the
secondary computer is a camera-based LDW system, a neural network
in the supervisory MCU may learn to override the LDW when
bicyclists or pedestrians are present and a lane departure is, in
fact, the safest maneuver. In embodiments that include a neural
network(s) running on the supervisory MCU, the supervisory MCU may
include at least one of a DLA or GPU suitable for running the
neural network(s) with associated memory. In preferred embodiments,
the supervisory MCU may comprise and/or be included as a component
of the SoC(s) 1104.
[0190] In other examples, ADAS system 1138 may include a secondary
computer that performs ADAS functionality using traditional rules
of computer vision. As such, the secondary computer may use classic
computer vision rules (if-then), and the presence of a neural
network(s) in the supervisory MCU may improve reliability, safety
and performance. For example, the diverse implementation and
intentional non-identity makes the overall system more
fault-tolerant, especially to faults caused by software (or
software-hardware interface) functionality. For example, if there
is a software bug or error in the software running on the primary
computer, and the non-identical software code running on the
secondary computer provides the same overall result, the
supervisory MCU may have greater confidence that the overall result
is correct, and the bug in software or hardware on primary computer
is not causing material error.
[0191] In some examples, the output of the ADAS system 1138 may be
fed into the primary computer's perception block and/or the primary
computer's dynamic driving task block. For example, if the ADAS
system 1138 indicates a forward crash warning due to an object
immediately ahead, the perception block may use this information
when identifying objects. In other examples, the secondary computer
may have its own neural network which is trained and thus reduces
the risk of false positives, as described herein.
[0192] The vehicle 1100 may further include the infotainment SoC
1130 (e.g., an in-vehicle infotainment system (IVI)). Although
illustrated and described as a SoC, the infotainment system may not
be a SoC, and may include two or more discrete components. The
infotainment SoC 1130 may include a combination of hardware and
software that may be used to provide audio (e.g., music, a personal
digital assistant, navigational instructions, news, radio, etc.),
video (e.g., TV, movies, streaming, etc.), phone (e.g., hands-free
calling), network connectivity (e.g., LTE, WiFi, etc.), and/or
information services (e.g., navigation systems, rear-parking
assistance, a radio data system, vehicle related information such
as fuel level, total distance covered, brake fuel level, oil level,
door open/close, air filter information, etc.) to the vehicle 1100.
For example, the infotainment SoC 1130 may radios, disk players,
navigation systems, video players, USB and Bluetooth connectivity,
carputers, in-car entertainment, WiFi, steering wheel audio
controls, hands free voice control, a heads-up display (HUD), an
HMI display 1134, a telematics device, a control panel (e.g., for
controlling and/or interacting with various components, features,
and/or systems), and/or other components. The infotainment SoC 1130
may further be used to provide information (e.g., visual and/or
audible) to a user(s) of the vehicle, such as information from the
ADAS system 1138, autonomous driving information such as planned
vehicle maneuvers, trajectories, surrounding environment
information (e.g., intersection information, vehicle information,
road information, etc.), and/or other information.
[0193] The infotainment SoC 1130 may include GPU functionality. The
infotainment SoC 1130 may communicate over the bus 1102 (e.g., CAN
bus, Ethernet, etc.) with other devices, systems, and/or components
of the vehicle 1100. In some examples, the infotainment SoC 1130
may be coupled to a supervisory MCU such that the GPU of the
infotainment system may perform some self-driving functions in the
event that the primary controller(s) 1136 (e.g., the primary and/or
backup computers of the vehicle 1100) fail. In such an example, the
infotainment SoC 1130 may put the vehicle 1100 into a chauffeur to
safe stop mode, as described herein.
[0194] The vehicle 1100 may further include an instrument cluster
1132 (e.g., a digital dash, an electronic instrument cluster, a
digital instrument panel, etc.). The instrument cluster 1132 may
include a controller and/or supercomputer (e.g., a discrete
controller or supercomputer). The instrument cluster 1132 may
include a set of instrumentation such as a speedometer, fuel level,
oil pressure, tachometer, odometer, turn indicators, gearshift
position indicator, seat belt warning light(s), parking-brake
warning light(s), engine-malfunction light(s), airbag (SRS) system
information, lighting controls, safety system controls, navigation
information, etc. In some examples, information may be displayed
and/or shared among the infotainment SoC 1130 and the instrument
cluster 1132. In other words, the instrument cluster 1132 may be
included as part of the infotainment SoC 1130, or vice versa.
[0195] FIG. 11D is a system diagram for communication between
cloud-based server(s) and the example autonomous vehicle 1100 of
FIG. 11A, in accordance with some embodiments of the present
disclosure. The system 1176 may include server(s) 1178, network(s)
1190, and vehicles, including the vehicle 1100. The server(s) 1178
may include a plurality of GPUs 1184(A)-1284(H) (collectively
referred to herein as GPUs 1184), PCIe switches 1182(A)-1182(H)
(collectively referred to herein as PCIe switches 1182), and/or
CPUs 1180(A)-1180(B) (collectively referred to herein as CPUs
1180). The GPUs 1184, the CPUs 1180, and the PCIe switches may be
interconnected with high-speed interconnects such as, for example
and without limitation, NVLink interfaces 1188 developed by NVIDIA
and/or PCIe connections 1186. In some examples, the GPUs 1184 are
connected via NVLink and/or NVSwitch SoC and the GPUs 1184 and the
PCIe switches 1182 are connected via PCIe interconnects. Although
eight GPUs 1184, two CPUs 1180, and two PCIe switches are
illustrated, this is not intended to be limiting. Depending on the
embodiment, each of the server(s) 1178 may include any number of
GPUs 1184, CPUs 1180, and/or PCIe switches. For example, the
server(s) 1178 may each include eight, sixteen, thirty-two, and/or
more GPUs 1184.
[0196] The server(s) 1178 may receive, over the network(s) 1190 and
from the vehicles, image data representative of images showing
unexpected or changed road conditions, such as recently commenced
road-work. The server(s) 1178 may transmit, over the network(s)
1190 and to the vehicles, neural networks 1192, updated neural
networks 1192, and/or map information 1194, including information
regarding traffic and road conditions. The updates to the map
information 1194 may include updates for the HD map 1122, such as
information regarding construction sites, potholes, detours,
flooding, and/or other obstructions. In some examples, the neural
networks 1192, the updated neural networks 1192, and/or the map
information 1194 may have resulted from new training and/or
experiences represented in data received from any number of
vehicles in the environment, and/or based at least in part on
training performed at a datacenter (e.g., using the server(s) 1178
and/or other servers).
[0197] The server(s) 1178 may be used to train machine learning
models (e.g., neural networks) based at least in part on training
data. The training data may be generated by the vehicles, and/or
may be generated in a simulation (e.g., using a game engine). In
some examples, the training data is tagged (e.g., where the neural
network benefits from supervised learning) and/or undergoes other
pre-processing, while in other examples the training data is not
tagged and/or pre-processed (e.g., where the neural network does
not require supervised learning). Once the machine learning models
are trained, the machine learning models may be used by the
vehicles (e.g., transmitted to the vehicles over the network(s)
1190, and/or the machine learning models may be used by the
server(s) 1178 to remotely monitor the vehicles.
[0198] In some examples, the server(s) 1178 may receive data from
the vehicles and apply the data to up-to-date real-time neural
networks for real-time intelligent inferencing. The server(s) 1178
may include deep-learning supercomputers and/or dedicated AI
computers powered by GPU(s) 1184, such as a DGX and DGX Station
machines developed by NVIDIA. However, in some examples, the
server(s) 1178 may include deep learning infrastructure that use
only CPU-powered datacenters.
[0199] The deep-learning infrastructure of the server(s) 1178 may
be capable of fast, real-time inferencing, and may use that
capability to evaluate and verify the health of the processors,
software, and/or associated hardware in the vehicle 1100. For
example, the deep-learning infrastructure may receive periodic
updates from the vehicle 1100, such as a sequence of images and/or
objects that the vehicle 1100 has located in that sequence of
images (e.g., via computer vision and/or other machine learning
object classification techniques). The deep-learning infrastructure
may run its own neural network to identify the objects and compare
them with the objects identified by the vehicle 1100 and, if the
results do not match and the infrastructure concludes that the AI
in the vehicle 1100 is malfunctioning, the server(s) 1178 may
transmit a signal to the vehicle 1100 instructing a fail-safe
computer of the vehicle 1100 to assume control, notify the
passengers, and complete a safe parking maneuver.
[0200] For inferencing, the server(s) 1178 may include the GPU(s)
1184 and one or more programmable inference accelerators (e.g.,
NVIDIA's TensorRT 3). The combination of GPU-powered servers and
inference acceleration may make real-time responsiveness possible.
In other examples, such as where performance is less critical,
servers powered by CPUs, FPGAs, and other processors may be used
for inferencing.
Example Computing Device
[0201] FIG. 12 is a block diagram of an example computing device
1200 suitable for use in implementing some embodiments of the
present disclosure, such as the object detector 106 and one or more
parts of the network 502. Computing device 1200 may include a bus
1202 that directly or indirectly couples the following devices:
memory 1204, one or more central processing units (CPUs) 1206, one
or more graphics processing units (GPUs) 1208, a communication
interface 1210, input/output (I/O) ports 1212, input/output
components 1214, a power supply 1216, and one or more presentation
components 1218 (e.g., display(s)).
[0202] Although the various blocks of FIG. 12 are shown as
connected via the bus 1202 with lines, this is not intended to be
limiting and is for clarity only. For example, in some embodiments,
a presentation component 1218, such as a display device, may be
considered an I/O component 1214 (e.g., if the display is a touch
screen). As another example, the CPUs 1206 and/or GPUs 1208 may
include memory (e.g., the memory 1204 may be representative of a
storage device in addition to the memory of the GPUs 1208, the CPUs
1206, and/or other components). In other words, the computing
device of FIG. 12 is merely illustrative. Distinction is not made
between such categories as "workstation," "server," "laptop,"
"desktop," "tablet," "client device," "mobile device," "hand-held
device," "game console," "electronic control unit (ECU)," "virtual
reality system," and/or other device or system types, as all are
contemplated within the scope of the computing device of FIG.
12.
[0203] The bus 1202 may represent one or more busses, such as an
address bus, a data bus, a control bus, or a combination thereof.
The bus 1202 may include one or more bus types, such as an industry
standard architecture (ISA) bus, an extended industry standard
architecture (EISA) bus, a video electronics standards association
(VESA) bus, a peripheral component interconnect (PCI) bus, a
peripheral component interconnect express (PCIe) bus, and/or
another type of bus.
[0204] The memory 1204 may include any of a variety of
computer-readable media. The computer-readable media may be any
available media that may be accessed by the computing device 1200.
The computer-readable media may include both volatile and
nonvolatile media, and removable and non-removable media. By way of
example, and not limitation, the computer-readable media may
comprise computer-storage media and communication media.
[0205] The computer-storage media may include both volatile and
nonvolatile media and/or removable and non-removable media
implemented in any method or technology for storage of information
such as computer-readable instructions, data structures, program
modules, and/or other data types. For example, the memory 1204 may
store computer-readable instructions (e.g., that represent a
program(s) and/or a program element(s), such as an operating
system). Computer-storage media may include, but is not limited to,
RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM,
digital versatile disks (DVD) or other optical disk storage,
magnetic cassettes, magnetic tape, magnetic disk storage or other
magnetic storage devices, or any other medium which may be used to
store the desired information and which may be accessed by
computing device 1200. As used herein, computer storage media does
not comprise signals per se.
[0206] The communication media may embody computer-readable
instructions, data structures, program modules, and/or other data
types in a modulated data signal such as a carrier wave or other
transport mechanism and includes any information delivery media.
The term "modulated data signal" may refer to a signal that has one
or more of its characteristics set or changed in such a manner as
to encode information in the signal. By way of example, and not
limitation, the communication media may include wired media such as
a wired network or direct-wired connection, and wireless media such
as acoustic, RF, infrared and other wireless media. Combinations of
any of the above should also be included within the scope of
computer-readable media.
[0207] The CPU(s) 1206 may be configured to execute the
computer-readable instructions to control one or more components of
the computing device 1200 to perform one or more of the methods
and/or processes (e.g., processes in FIGS. 2, 5A, and 7-9)
described herein. The CPU(s) 1206 may each include one or more
cores (e.g., one, two, four, eight, twenty-eight, seventy-two,
etc.) that are capable of handling a multitude of software threads
simultaneously. The CPU(s) 1206 may include any type of processor,
and may include different types of processors depending on the type
of computing device 1200 implemented (e.g., processors with fewer
cores for mobile devices and processors with more cores for
servers). For example, depending on the type of computing device
1200, the processor may be an ARM processor implemented using
Reduced Instruction Set Computing (RISC) or an x86 processor
implemented using Complex Instruction Set Computing (CISC). The
computing device 1200 may include one or more CPUs 1206 in addition
to one or more microprocessors or supplementary co-processors, such
as math co-processors.
[0208] The GPU(s) 1208 may be used by the computing device 1200 to
render graphics (e.g., 3D graphics). The GPU(s) 1208 may include
hundreds or thousands of cores that are capable of handling
hundreds or thousands of software threads simultaneously. The
GPU(s) 1208 may generate pixel data for output images in response
to rendering commands (e.g., rendering commands from the CPU(s)
1206 received via a host interface). The GPU(s) 1208 may include
graphics memory, such as display memory, for storing pixel data.
The display memory may be included as part of the memory 1204. The
GPU(s) 1208 may include two or more GPUs operating in parallel
(e.g., via a link). When combined together, each GPU 1208 may
generate pixel data for different portions of an output image or
for different output images (e.g., a first GPU for a first image
and a second GPU for a second image). Each GPU may include its own
memory, or may share memory with other GPUs.
[0209] In examples where the computing device 1200 does not include
the GPU(s) 1208, the CPU(s) 1206 may be used to render
graphics.
[0210] The communication interface 1210 may include one or more
receivers, transmitters, and/or transceivers that enable the
computing device 1200 to communicate with other computing devices
via an electronic communication network, included wired and/or
wireless communications. The communication interface 1210 may
include components and functionality to enable communication over
any of a number of different networks, such as wireless networks
(e.g., Wi-Fi, Z-Wave, Bluetooth, Bluetooth LE, ZigBee, etc.), wired
networks (e.g., communicating over Ethernet), low-power wide-area
networks (e.g., LoRaWAN, SigFox, etc.), and/or the Internet.
[0211] The I/O ports 1212 may enable the computing device 1200 to
be logically coupled to other devices including the I/O components
1214, the presentation component(s) 1218, and/or other components,
some of which may be built in to (e.g., integrated in) the
computing device 1200. Illustrative I/O components 1214 include a
microphone, mouse, keyboard, joystick, game pad, game controller,
satellite dish, scanner, printer, wireless device, etc. The I/O
components 1214 may provide a natural user interface (NUI) that
processes air gestures, voice, or other physiological inputs
generated by a user. In some instances, inputs may be transmitted
to an appropriate network element for further processing. An NUI
may implement any combination of speech recognition, stylus
recognition, facial recognition, biometric recognition, gesture
recognition both on screen and adjacent to the screen, air
gestures, head and eye tracking, and touch recognition (as
described in more detail below) associated with a display of the
computing device 1200. The computing device 1200 may be include
depth cameras, such as stereoscopic camera systems, infrared camera
systems, RGB camera systems, touchscreen technology, and
combinations of these, for gesture detection and recognition.
Additionally, the computing device 1200 may include accelerometers
or gyroscopes (e.g., as part of an inertia measurement unit (IMU))
that enable detection of motion. In some examples, the output of
the accelerometers or gyroscopes may be used by the computing
device 1200 to render immersive augmented reality or virtual
reality.
[0212] The power supply 1216 may include a hard-wired power supply,
a battery power supply, or a combination thereof. The power supply
1216 may provide power to the computing device 1200 to enable the
components of the computing device 1200 to operate.
[0213] The presentation component(s) 1218 may include a display
(e.g., a monitor, a touch screen, a television screen, a
heads-up-display (HUD), other display types, or a combination
thereof), speakers, and/or other presentation components. The
presentation component(s) 1218 may receive data from other
components (e.g., the GPU(s) 1208, the CPU(s) 1206, etc.), and
output the data (e.g., as an image, video, sound, etc.). In one
aspect, the presentation component(s) may display an image (e.g.,
525) that delineates a parking space, an entry to a parking space,
or any combination thereof.
[0214] The disclosure may be described in the general context of
computer code or machine-useable instructions, including
computer-executable instructions such as program modules, being
executed by a computer or other machine, such as a personal data
assistant or other handheld device. Generally, program modules
including routines, programs, objects, components, data structures,
etc., refer to code that perform particular tasks or implement
particular abstract data types. The disclosure may be practiced in
a variety of system configurations, including hand-held devices,
consumer electronics, general-purpose computers, more specialty
computing devices, etc. The disclosure may also be practiced in
distributed computing environments where tasks are performed by
remote-processing devices that are linked through a communications
network.
[0215] As used herein, a recitation of "and/or" with respect to two
or more elements should be interpreted to mean only one element, or
a combination of elements. For example, "element A, element B,
and/or element C" may include only element A, only element B, only
element C, element A and element B, element A and element C,
element B and element C, or elements A, B, and C. In addition, "at
least one of element A or element B" may include at least one of
element A, at least one of element B, or at least one of element A
and at least one of element B. Further, "at least one of element A
and element B" may include at least one of element A, at least one
of element B, or at least one of element A and at least one of
element B.
[0216] The subject matter of the present disclosure is described
with specificity herein to meet statutory requirements. However,
the description itself is not intended to limit the scope of this
disclosure. Rather, the inventors have contemplated that the
claimed subject matter might also be embodied in other ways, to
include different steps or combinations of steps similar to the
ones described in this document, in conjunction with other present
or future technologies. Moreover, although the terms "step" and/or
"block" may be used herein to connote different elements of methods
employed, the terms should not be interpreted as implying any
particular order among or between various steps herein disclosed
unless and except when the order of individual steps is explicitly
described.
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